~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -------------------------------------------------------------------------------- ################################################################################ ##### TS-CONFIRMED Cases -- Data dated: 2020-04-05 :: 2020-04-06 17:54:21 ################################################################################ Total number of Countries/Regions affected: 183 Total number of Cities/Provinces affected: 83 -------------------------------------------------------------------------------- Country.Region Province.State Totals GlobalPerc 226 US 337072 26.50 202 Spain 131646 10.35 138 Italy 128948 10.14 121 Germany 100123 7.87 117 France 92839 7.30 63 China Hubei 67803 5.33 134 Iran 58226 4.58 224 United Kingdom 47806 3.76 214 Turkey 27069 2.13 207 Switzerland 21100 1.66 -------------------------------------------------------------------------------- Global Perc. Average: 0.381412213740458 Global Perc. Average in top 10 : 7.962 ================================================================================ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -------------------------------------------------------------------------------- ################################################################################ ##### TS-RECOVERED Cases -- Data dated: 2020-04-05 :: 2020-04-06 17:54:22 ################################################################################ Total number of Countries/Regions affected: 183 Total number of Cities/Provinces affected: 68 -------------------------------------------------------------------------------- Country.Region Province.State Totals 54 China Hubei 63945 200 Spain 38080 113 Germany 28700 132 Italy 21815 128 Iran 19736 226 US 17448 109 France 16183 138 Korea, South 6463 205 Switzerland 6415 24 Belgium 3751 ================================================================================ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -------------------------------------------------------------------------------- ################################################################################ ##### TS-DEATHS Cases -- Data dated: 2020-04-05 :: 2020-04-06 17:54:23 ################################################################################ Total number of Countries/Regions affected: 183 Total number of Cities/Provinces affected: 83 -------------------------------------------------------------------------------- Country.Region Province.State Totals Perc 138 Italy 15887 12.32 202 Spain 12641 9.60 226 US 9619 2.85 117 France 8078 8.70 224 United Kingdom 4934 10.32 134 Iran 3603 6.19 63 China Hubei 3210 4.73 170 Netherlands 1766 9.89 121 Germany 1584 1.58 24 Belgium 1447 7.35 -------------------------------------------------------------------------------- Global Perc. Average: Inf Global Perc. Average in top 10 : 7.353 ================================================================================ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ################################################################################ ##### AGGREGATED Data -- ORDERED BY CONFIRMED Cases -- Data dated: 2020-04-05 :: 2020-04-06 17:54:24 ################################################################################ Total number of Countries/Regions affected: 183 Total number of Cities/Provinces affected: 138 -------------------------------------------------------------------------------- Country_Region Province_State Confirmed Deaths Perc.Confirmed Recovered Perc.Deaths Active 2737 Spain 131646 12641 10.35 38080 9.60 80925 2666 Italy 128948 15887 10.14 21815 12.32 91246 2647 Germany 100123 1584 7.87 28700 1.58 69839 2643 France 92839 8078 7.30 16183 8.70 68578 2532 China Hubei 67803 3210 5.33 63945 4.73 648 1611 US New York 67551 2256 5.31 0 3.34 0 2662 Iran 58226 3603 4.58 19736 6.19 34887 2755 United Kingdom 47806 4934 3.76 135 10.32 42737 2751 Turkey 27069 574 2.13 1042 2.12 25453 2742 Switzerland 21100 715 1.66 6415 3.39 13970 Perc.Recovered 2737 28.93 2666 16.92 2647 28.66 2643 17.43 2532 94.31 1611 0.00 2662 33.90 2755 0.28 2751 3.85 2742 30.40 ================================================================================ ################################################################################ ##### AGGREGATED Data -- ORDERED BY DEATHS Cases -- Data dated: 2020-04-05 :: 2020-04-06 17:54:24 ################################################################################ Total number of Countries/Regions affected: 183 Total number of Cities/Provinces affected: 138 -------------------------------------------------------------------------------- Country_Region Province_State Confirmed Deaths Perc.Confirmed Recovered Perc.Deaths Active 2666 Italy 128948 15887 10.14 21815 12.32 91246 2737 Spain 131646 12641 10.35 38080 9.60 80925 2643 France 92839 8078 7.30 16183 8.70 68578 2755 United Kingdom 47806 4934 3.76 135 10.32 42737 2662 Iran 58226 3603 4.58 19736 6.19 34887 2532 China Hubei 67803 3210 5.33 63945 4.73 648 1611 US New York 67551 2256 5.31 0 3.34 0 2702 Netherlands 17851 1766 1.40 250 9.89 15835 2647 Germany 100123 1584 7.87 28700 1.58 69839 2600 Belgium 19691 1447 1.55 3751 7.35 14493 Perc.Recovered 2666 16.92 2737 28.93 2643 17.43 2755 0.28 2662 33.90 2532 94.31 1611 0.00 2702 1.40 2647 28.66 2600 19.05 ================================================================================ ################################################################################ ##### AGGREGATED Data -- ORDERED BY RECOVERED Cases -- Data dated: 2020-04-05 :: 2020-04-06 17:54:25 ################################################################################ Total number of Countries/Regions affected: 183 Total number of Cities/Provinces affected: 138 -------------------------------------------------------------------------------- Country_Region Province_State Confirmed Deaths Perc.Confirmed Recovered Perc.Deaths Active 2532 China Hubei 67803 3210 5.33 63945 4.73 648 2737 Spain 131646 12641 10.35 38080 9.60 80925 2647 Germany 100123 1584 7.87 28700 1.58 69839 2666 Italy 128948 15887 10.14 21815 12.32 91246 2662 Iran 58226 3603 4.58 19736 6.19 34887 2561 US Recovered 0 0 0.00 17448 NaN 0 2643 France 92839 8078 7.30 16183 8.70 68578 2672 Korea, South 10237 183 0.80 6463 1.79 3591 2742 Switzerland 21100 715 1.66 6415 3.39 13970 2600 Belgium 19691 1447 1.55 3751 7.35 14493 Perc.Recovered 2532 94.31 2737 28.93 2647 28.66 2666 16.92 2662 33.90 2561 Inf 2643 17.43 2672 63.13 2742 30.40 2600 19.05 ================================================================================ ################################################################################ ##### AGGREGATED Data -- ORDERED BY ACTIVE Cases -- Data dated: 2020-04-05 :: 2020-04-06 17:54:25 ################################################################################ Total number of Countries/Regions affected: 183 Total number of Cities/Provinces affected: 138 -------------------------------------------------------------------------------- Country_Region Province_State Confirmed Deaths Perc.Confirmed Recovered Perc.Deaths Active 2666 Italy 128948 15887 10.14 21815 12.32 91246 2737 Spain 131646 12641 10.35 38080 9.60 80925 2647 Germany 100123 1584 7.87 28700 1.58 69839 2643 France 92839 8078 7.30 16183 8.70 68578 2755 United Kingdom 47806 4934 3.76 135 10.32 42737 2662 Iran 58226 3603 4.58 19736 6.19 34887 2751 Turkey 27069 574 2.13 1042 2.12 25453 2702 Netherlands 17851 1766 1.40 250 9.89 15835 2600 Belgium 19691 1447 1.55 3751 7.35 14493 2742 Switzerland 21100 715 1.66 6415 3.39 13970 Perc.Recovered 2666 16.92 2737 28.93 2647 28.66 2643 17.43 2755 0.28 2662 33.90 2751 3.85 2702 1.40 2600 19.05 2742 30.40 ================================================================================ 1.66 6415 3.39 13970 Perc.Recovered 2737 28.93 2666 16.92 2647 28.66 2643 17.43 2532 94.31 1611 0.00 2662 33.90 2755 0.28 2751 3.85 2742 30.40 ================================================================================ ################################################################################ ##### AGGREGATED Data -- ORDERED BY DEATHS Cases -- Data dated: 2020-04-05 :: 2020-04-06 17:54:24 ################################################################################ Total number of Countries/Regions affected: 183 Total number of Cities/Provinces affected: 138 -------------------------------------------------------------------------------- Country_Region Province_State Confirmed Deaths Perc.Confirmed Recovered Perc.Deaths Active 2666 Italy 128948 15887 10.14 21815 12.32 91246 2737 Spain 131646 12641 10.35 38080 9.60 80925 2643 France 92839 8078 7.30 16183 8.70 68578 2755 United Kingdom 47806 4934 3.76 135 10.32 42737 2662 Iran 58226 3603 4.58 19736 6.19 34887 2532 China Hubei 67803 3210 5.33 63945 4.73 648 1611 US New York 67551 2256 5.31 0 3.34 0 2702 Netherlands 17851 1766 1.40 250 9.89 15835 2647 Germany 100123 1584 7.87 28700 1.58 69839 2600 Belgium 19691 1447 1.55 3751 7.35 14493 Perc.Recovered 2666 16.92 2737 28.93 2643 17.43 2755 0.28 2662 33.90 2532 94.31 1611 0.00 2702 1.40 2647 28.66 2600 19.05 ================================================================================ ################################################################################ ##### AGGREGATED Data -- ORDERED BY RECOVERED Cases -- Data dated: 2020-04-05 :: 2020-04-06 17:54:25 ################################################################################ Total number of Countries/Regions affected: 183 Total number of Cities/Provinces affected: 138 -------------------------------------------------------------------------------- Country_Region Province_State Confirmed Deaths Perc.Confirmed Recovered Perc.Deaths Active 2532 China Hubei 67803 3210 5.33 63945 4.73 648 2737 Spain 131646 12641 10.35 38080 9.60 80925 2647 Germany 100123 1584 7.87 28700 1.58 69839 2666 Italy 128948 15887 10.14 21815 12.32 91246 2662 Iran 58226 3603 4.58 19736 6.19 34887 2561 US Recovered 0 0 0.00 17448 NaN 0 2643 France 92839 8078 7.30 16183 8.70 68578 2672 Korea, South 10237 183 0.80 6463 1.79 3591 2742 Switzerland 21100 715 1.66 6415 3.39 13970 2600 Belgium 19691 1447 1.55 3751 7.35 14493 Perc.Recovered 2532 94.31 2737 28.93 2647 28.66 2666 16.92 2662 33.90 2561 Inf 2643 17.43 2672 63.13 2742 30.40 2600 19.05 ================================================================================ ################################################################################ ##### AGGREGATED Data -- ORDERED BY ACTIVE Cases -- Data dated: 2020-04-05 :: 2020-04-06 17:54:25 ################################################################################ Total number of Countries/Regions affected: 183 Total number of Cities/Provinces affected: 138 -------------------------------------------------------------------------------- Country_Region Province_State Confirmed Deaths Perc.Confirmed Recovered Perc.Deaths Active 2666 Italy 128948 15887 10.14 21815 12.32 91246 2737 Spain 131646 12641 10.35 38080 9.60 80925 2647 Germany 100123 1584 7.87 28700 1.58 69839 2643 France 92839 8078 7.30 16183 8.70 68578 2755 United Kingdom 47806 4934 3.76 135 10.32 42737 2662 Iran 58226 3603 4.58 19736 6.19 34887 2751 Turkey 27069 574 2.13 1042 2.12 25453 2702 Netherlands 17851 1766 1.40 250 9.89 15835 2600 Belgium 19691 1447 1.55 3751 7.35 14493 2742 Switzerland 21100 715 1.66 6415 3.39 13970 Perc.Recovered 2666 16.92 2737 28.93 2647 28.66 2643 17.43 2755 0.28 2662 33.90 2751 3.85 2702 1.40 2600 19.05 2742 30.40 ================================================================================ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -------------------------------------------------------------------------------- FRANCE -- 93773 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -16864 -11700 -2337 8003 58407 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -15286.28 3521.05 -4.341 4.49e-05 *** x.var 675.36 80.51 8.389 2.62e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 15090 on 73 degrees of freedom Multiple R-squared: 0.4908, Adjusted R-squared: 0.4838 F-statistic: 70.37 on 1 and 73 DF, p-value: 2.62e-12 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.1944 -0.5656 0.2640 0.6149 1.4663 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.722953 0.215055 -3.362 0.00124 ** x.var 0.160726 0.004917 32.686 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9219 on 73 degrees of freedom Multiple R-squared: 0.936, Adjusted R-squared: 0.9352 F-statistic: 1068 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -46.965 -18.684 -4.657 -0.673 30.076 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.8189260 0.0111904 73.18 <2e-16 *** x.var 0.1430791 0.0001625 880.37 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 2160022 on 74 degrees of freedom Residual deviance: 23499 on 73 degrees of freedom AIC: 24037 Number of Fisher Scoring iterations: 4 -------------------------------------------------------------------------------- FRANCE -- 93773 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -16864 -11700 -2337 8003 58407 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -15286.28 3521.05 -4.341 4.49e-05 *** x.var 675.36 80.51 8.389 2.62e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 15090 on 73 degrees of freedom Multiple R-squared: 0.4908, Adjusted R-squared: 0.4838 F-statistic: 70.37 on 1 and 73 DF, p-value: 2.62e-12 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.1944 -0.5656 0.2640 0.6149 1.4663 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.722953 0.215055 -3.362 0.00124 ** x.var 0.160726 0.004917 32.686 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9219 on 73 degrees of freedom Multiple R-squared: 0.936, Adjusted R-squared: 0.9352 F-statistic: 1068 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -46.965 -18.684 -4.657 -0.673 30.076 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.8189260 0.0111904 73.18 <2e-16 *** x.var 0.1430791 0.0001625 880.37 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 2160022 on 74 degrees of freedom Residual deviance: 23499 on 73 degrees of freedom AIC: 24037 Number of Fisher Scoring iterations: 4 -------------------------------------------------------------------------------- Province.State Country.Region Lat Long 2020-01-22 2020-01-23 1 Afghanistan 33.0000 65.0000 0 0 2 Albania 41.1533 20.1683 0 0 3 Algeria 28.0339 1.6596 0 0 4 Andorra 42.5063 1.5218 0 0 5 Angola -11.2027 17.8739 0 0 6 Antigua and Barbuda 17.0608 -61.7964 0 0 7 Argentina -38.4161 -63.6167 0 0 8 Armenia 40.0691 45.0382 0 0 9 Australian Capital Territory Australia -35.4735 149.0124 0 0 10 New South Wales Australia -33.8688 151.2093 0 0 11 Northern Territory Australia -12.4634 130.8456 0 0 12 Queensland Australia -28.0167 153.4000 0 0 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 10 0 0 3 4 4 4 4 4 11 0 0 0 0 0 0 0 0 12 0 0 0 0 0 1 3 2 2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 10 4 4 4 4 4 4 4 4 11 0 0 0 0 0 0 0 0 12 3 2 2 3 3 4 5 5 2020-02-09 2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 10 4 4 4 4 4 4 4 4 11 0 0 0 0 0 0 0 0 12 5 5 5 5 5 5 5 5 2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 1 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 10 4 4 4 4 4 4 4 4 11 0 0 0 0 0 0 0 0 12 5 5 5 5 5 5 5 5 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03 1 1 1 1 1 1 1 1 1 2 0 0 0 0 0 0 0 0 3 1 1 1 1 1 1 3 5 4 0 0 0 0 0 0 1 1 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 1 8 0 0 0 0 0 1 1 1 9 0 0 0 0 0 0 0 0 10 4 4 4 4 4 6 6 13 11 0 0 0 0 0 0 0 0 12 5 5 5 5 9 9 9 11 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11 1 1 1 1 1 4 4 5 7 2 0 0 0 0 0 2 10 12 3 12 12 17 17 19 20 20 20 4 1 1 1 1 1 1 1 1 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 1 1 2 8 12 12 17 19 8 1 1 1 1 1 1 1 1 9 0 0 0 0 0 0 0 0 10 22 22 26 28 38 48 55 65 11 1 1 0 0 0 0 1 1 12 11 13 13 13 15 15 18 20 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18 2020-03-19 1 7 7 11 16 21 22 22 22 2 23 33 38 42 51 55 59 64 3 24 26 37 48 54 60 74 87 4 1 1 1 1 2 39 39 53 5 0 0 0 0 0 0 0 0 6 0 1 1 1 1 1 1 1 7 19 31 34 45 56 68 79 97 8 4 8 18 26 52 78 84 115 9 0 1 1 1 2 2 3 4 10 65 92 112 134 171 210 267 307 11 1 1 1 1 1 1 1 1 12 20 35 46 61 68 78 94 144 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 1 24 24 40 40 74 84 94 110 2 70 76 89 104 123 146 174 186 3 90 139 201 230 264 302 367 409 4 75 88 113 133 164 188 224 267 5 1 2 2 3 3 3 4 4 6 1 1 1 3 3 3 7 7 7 128 158 266 301 387 387 502 589 8 136 160 194 235 249 265 290 329 9 6 9 19 32 39 39 53 62 10 353 436 669 669 818 1029 1219 1405 11 3 3 5 5 6 6 12 12 12 184 221 259 319 397 443 493 555 2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 1 110 120 170 174 237 273 281 299 2 197 212 223 243 259 277 304 333 3 454 511 584 716 847 986 1171 1251 4 308 334 370 376 390 428 439 466 5 5 7 7 7 8 8 8 10 6 7 7 7 7 7 9 15 15 7 690 745 820 1054 1054 1133 1265 1451 8 407 424 482 532 571 663 736 770 9 71 77 78 80 84 87 91 93 10 1617 1791 2032 2032 2182 2298 2389 2493 11 15 15 15 17 19 21 22 26 12 625 656 689 743 781 835 873 900 2020-04-05 1 349 2 361 3 1320 4 501 5 14 6 15 7 1451 8 822 9 96 10 2580 11 27 12 907 [ reached 'max' / getOption("max.print") -- omitted 250 rows ] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -------------------------------------------------------------------------------- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -------------------------------------------------------------------------------- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AFGHANISTAN -- 7 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.9690 -1.0659 -0.1839 0.7987 4.2787 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.255495 0.324274 -3.872 0.000233 *** x.var 0.053741 0.007415 7.248 3.61e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.39 on 73 degrees of freedom Multiple R-squared: 0.4185, Adjusted R-squared: 0.4105 F-statistic: 52.53 on 1 and 73 DF, p-value: 3.605e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.7396 -0.3699 -0.0601 0.2897 1.0600 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.459550 0.109936 -4.180 7.99e-05 *** x.var 0.019986 0.002514 7.951 1.74e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4713 on 73 degrees of freedom Multiple R-squared: 0.4641, Adjusted R-squared: 0.4567 F-statistic: 63.22 on 1 and 73 DF, p-value: 1.742e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.12663 -0.29950 -0.06039 -0.01114 1.60616 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.42782 1.67465 -6.824 8.85e-12 *** x.var 0.18289 0.02385 7.670 1.72e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 206.207 on 74 degrees of freedom Residual deviance: 15.375 on 73 degrees of freedom AIC: 65.779 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- ALBANIA -- 20 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -4.2324 -3.0981 -0.6552 1.9609 11.9220 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.62090 0.86477 -4.187 7.79e-05 *** x.var 0.15809 0.01977 7.995 1.44e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.707 on 73 degrees of freedom Multiple R-squared: 0.4669, Adjusted R-squared: 0.4596 F-statistic: 63.92 on 1 and 73 DF, p-value: 1.437e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.9823 -0.4400 -0.0928 0.4231 1.1727 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.761148 0.134591 -5.655 2.85e-07 *** x.var 0.035581 0.003077 11.562 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.577 on 73 degrees of freedom Multiple R-squared: 0.6468, Adjusted R-squared: 0.6419 F-statistic: 133.7 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.17667 -0.32741 -0.10049 -0.02586 0.89331 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.39268 0.80571 -10.42 <2e-16 *** x.var 0.15521 0.01162 13.36 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 532.542 on 74 degrees of freedom Residual deviance: 12.154 on 73 degrees of freedom AIC: 101.71 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- ALGERIA -- 152 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -21.466 -13.668 -5.062 7.474 111.686 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -18.2951 5.3750 -3.404 0.00108 ** x.var 0.7815 0.1229 6.358 1.57e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 23.04 on 73 degrees of freedom Multiple R-squared: 0.3564, Adjusted R-squared: 0.3476 F-statistic: 40.43 on 1 and 73 DF, p-value: 1.574e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.67701 -0.69155 0.06762 0.69784 1.89956 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.230711 0.209896 -5.863 1.22e-07 *** x.var 0.058154 0.004799 12.117 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8998 on 73 degrees of freedom Multiple R-squared: 0.6679, Adjusted R-squared: 0.6634 F-statistic: 146.8 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.9042 -0.5162 -0.1007 -0.0197 1.8761 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.016620 0.449578 -20.06 <2e-16 *** x.var 0.186630 0.006392 29.20 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 2842.129 on 74 degrees of freedom Residual deviance: 42.138 on 73 degrees of freedom AIC: 163.19 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- ANDORRA -- 18 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -4.0727 -2.4342 -0.6827 1.2949 12.2323 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.70739 0.83141 -3.256 0.00171 ** x.var 0.11300 0.01901 5.944 8.78e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.564 on 73 degrees of freedom Multiple R-squared: 0.3261, Adjusted R-squared: 0.3169 F-statistic: 35.33 on 1 and 73 DF, p-value: 8.778e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.92676 -0.45900 -0.09238 0.32264 1.63841 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.59029 0.15000 -3.935 0.000188 *** x.var 0.02528 0.00343 7.372 2.12e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.643 on 73 degrees of freedom Multiple R-squared: 0.4267, Adjusted R-squared: 0.4189 F-statistic: 54.34 on 1 and 73 DF, p-value: 2.118e-10 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.61496 -0.22275 -0.02633 -0.00277 1.46706 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -15.03743 1.60189 -9.387 <2e-16 *** x.var 0.24381 0.02241 10.882 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 468.556 on 74 degrees of freedom Residual deviance: 15.488 on 73 degrees of freedom AIC: 71.481 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- ANGOLA -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.65555 -0.37345 -0.09134 0.19076 1.32920 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.366126 0.123354 -2.968 0.00405 ** x.var 0.015249 0.002821 5.406 7.75e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5288 on 73 degrees of freedom Multiple R-squared: 0.2859, Adjusted R-squared: 0.2761 F-statistic: 29.23 on 1 and 73 DF, p-value: 7.745e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.36010 -0.20514 -0.05017 0.10479 0.73014 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.201115 0.067759 -2.968 0.00405 ** x.var 0.008376 0.001549 5.406 7.75e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2905 on 73 degrees of freedom Multiple R-squared: 0.2859, Adjusted R-squared: 0.2761 F-statistic: 29.23 on 1 and 73 DF, p-value: 7.745e-07 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.97587 -0.12297 -0.01361 -0.00133 1.39991 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -17.58007 4.51101 -3.897 9.73e-05 *** x.var 0.25131 0.06299 3.989 6.62e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 71.6175 on 74 degrees of freedom Residual deviance: 9.7356 on 73 degrees of freedom AIC: 34.645 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- ANTIGUA AND BARBUDA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- ARGENTINA -- 44 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -8.572 -6.448 -1.518 3.946 27.807 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.22486 1.87820 -3.847 0.000254 *** x.var 0.31223 0.04295 7.270 3.27e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 8.052 on 73 degrees of freedom Multiple R-squared: 0.42, Adjusted R-squared: 0.412 F-statistic: 52.86 on 1 and 73 DF, p-value: 3.274e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.1250 -0.5083 -0.1437 0.5127 1.4029 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.938959 0.156869 -5.986 7.4e-08 *** x.var 0.044868 0.003587 12.509 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6725 on 73 degrees of freedom Multiple R-squared: 0.6819, Adjusted R-squared: 0.6775 F-statistic: 156.5 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.38615 -0.34264 -0.10197 -0.02345 1.01446 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.621259 0.629793 -13.69 <2e-16 *** x.var 0.168098 0.009024 18.63 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1080.936 on 74 degrees of freedom Residual deviance: 13.862 on 73 degrees of freedom AIC: 122.38 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- ARMENIA -- 7 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.6881 -0.9044 -0.2478 0.5359 4.9730 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.023063 0.337830 -3.028 0.0034 ** x.var 0.042361 0.007725 5.484 5.68e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.448 on 73 degrees of freedom Multiple R-squared: 0.2918, Adjusted R-squared: 0.2821 F-statistic: 30.07 on 1 and 73 DF, p-value: 5.684e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.61167 -0.32981 -0.04795 0.18821 1.34588 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.363424 0.107313 -3.387 0.00114 ** x.var 0.015236 0.002454 6.209 2.93e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.46 on 73 degrees of freedom Multiple R-squared: 0.3456, Adjusted R-squared: 0.3366 F-statistic: 38.55 on 1 and 73 DF, p-value: 2.933e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.17333 -0.12057 -0.00945 -0.00074 1.03686 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -18.26702 2.99170 -6.106 1.02e-09 *** x.var 0.27501 0.04159 6.612 3.78e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 186.7666 on 74 degrees of freedom Residual deviance: 8.7071 on 73 degrees of freedom AIC: 46.179 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- AUSTRALIA -- 35 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -5.862 -3.787 -1.447 2.658 20.841 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -5.70378 1.19567 -4.770 9.17e-06 *** x.var 0.26484 0.02734 9.687 9.70e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.126 on 73 degrees of freedom Multiple R-squared: 0.5624, Adjusted R-squared: 0.5565 F-statistic: 93.84 on 1 and 73 DF, p-value: 9.705e-15 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.98980 -0.32056 -0.01019 0.38813 0.87224 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.87513 0.11020 -7.941 1.82e-11 *** x.var 0.04782 0.00252 18.977 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4724 on 73 degrees of freedom Multiple R-squared: 0.8315, Adjusted R-squared: 0.8292 F-statistic: 360.1 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.1304 -0.4376 -0.2036 0.1212 1.0327 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -4.761382 0.413981 -11.50 <2e-16 *** x.var 0.110599 0.006172 17.92 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 750.330 on 74 degrees of freedom Residual deviance: 19.511 on 73 degrees of freedom AIC: 153.84 Number of Fisher Scoring iterations: 4 -------------------------------------------------------------------------------- AUSTRIA -- 204 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -42.678 -27.393 -6.107 16.551 133.349 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -32.3438 8.7748 -3.686 0.000435 *** x.var 1.3733 0.2006 6.844 2.02e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 37.62 on 73 degrees of freedom Multiple R-squared: 0.3909, Adjusted R-squared: 0.3825 F-statistic: 46.85 on 1 and 73 DF, p-value: 2.022e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.80396 -0.88009 -0.08365 0.84022 1.92617 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.381802 0.252883 -5.464 6.15e-07 *** x.var 0.063715 0.005782 11.019 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.084 on 73 degrees of freedom Multiple R-squared: 0.6245, Adjusted R-squared: 0.6194 F-statistic: 121.4 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -4.0596 -0.9671 -0.1798 -0.0351 2.8007 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.279654 0.363559 -25.52 <2e-16 *** x.var 0.198255 0.005148 38.51 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 5152.71 on 74 degrees of freedom Residual deviance: 100.48 on 73 degrees of freedom AIC: 225.02 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- AZERBAIJAN -- 7 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.5634 -0.8996 -0.2525 0.6740 4.1256 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.222342 0.268215 -4.557 2.04e-05 *** x.var 0.054623 0.006133 8.907 2.79e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.15 on 73 degrees of freedom Multiple R-squared: 0.5208, Adjusted R-squared: 0.5142 F-statistic: 79.33 on 1 and 73 DF, p-value: 2.787e-13 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.66751 -0.25322 -0.04207 0.29179 0.87022 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.483650 0.088721 -5.451 6.47e-07 *** x.var 0.022572 0.002029 11.126 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3803 on 73 degrees of freedom Multiple R-squared: 0.6291, Adjusted R-squared: 0.624 F-statistic: 123.8 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.80610 -0.32761 -0.10887 -0.03388 0.85096 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.93128 1.14476 -6.928 4.26e-12 *** x.var 0.13347 0.01673 7.976 1.51e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 177.243 on 74 degrees of freedom Residual deviance: 10.412 on 73 degrees of freedom AIC: 78.017 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- BAHAMAS -- 4 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.5054 -0.2980 -0.0906 0.1167 3.4498 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.27928 0.14695 -1.900 0.06132 . x.var 0.01121 0.00336 3.336 0.00134 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.63 on 73 degrees of freedom Multiple R-squared: 0.1323, Adjusted R-squared: 0.1204 F-statistic: 11.13 on 1 and 73 DF, p-value: 0.001339 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.24197 -0.14292 -0.04387 0.05517 1.34605 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.132804 0.062498 -2.125 0.036977 * x.var 0.005354 0.001429 3.747 0.000356 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2679 on 73 degrees of freedom Multiple R-squared: 0.1613, Adjusted R-squared: 0.1498 F-statistic: 14.04 on 1 and 73 DF, p-value: 0.0003557 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.68570 -0.00441 -0.00002 0.00000 0.73307 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -44.3651 13.8639 -3.200 0.00137 ** x.var 0.6131 0.1878 3.265 0.00109 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 64.4118 on 74 degrees of freedom Residual deviance: 2.5312 on 73 degrees of freedom AIC: 19.063 Number of Fisher Scoring iterations: 10 -------------------------------------------------------------------------------- BAHRAIN -- 4 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.63177 -0.82486 -0.04605 0.79796 1.85671 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.130450 0.233931 -4.832 7.25e-06 *** x.var 0.051152 0.005349 9.563 1.65e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.003 on 73 degrees of freedom Multiple R-squared: 0.5561, Adjusted R-squared: 0.55 F-statistic: 91.45 on 1 and 73 DF, p-value: 1.65e-14 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.71386 -0.30557 -0.02969 0.33446 0.67488 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.47791 0.09227 -5.18 1.90e-06 *** x.var 0.02207 0.00211 10.46 3.61e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3955 on 73 degrees of freedom Multiple R-squared: 0.5999, Adjusted R-squared: 0.5944 F-statistic: 109.4 on 1 and 73 DF, p-value: 3.605e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.28618 -0.41381 -0.13894 -0.04669 1.39748 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.38354 1.08950 -6.777 1.23e-11 *** x.var 0.12468 0.01603 7.775 7.52e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 170.428 on 74 degrees of freedom Residual deviance: 19.671 on 73 degrees of freedom AIC: 83.079 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- BANGLADESH -- 9 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.4361 -1.3028 -0.0964 1.1100 5.1747 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.658378 0.368339 -4.502 2.49e-05 *** x.var 0.073115 0.008422 8.681 7.38e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.579 on 73 degrees of freedom Multiple R-squared: 0.508, Adjusted R-squared: 0.5012 F-statistic: 75.36 on 1 and 73 DF, p-value: 7.384e-13 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.85832 -0.38936 0.00356 0.39648 0.96262 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.56125 0.11460 -4.897 5.66e-06 *** x.var 0.02535 0.00262 9.674 1.03e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4913 on 73 degrees of freedom Multiple R-squared: 0.5618, Adjusted R-squared: 0.5558 F-statistic: 93.58 on 1 and 73 DF, p-value: 1.027e-14 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.20063 -0.41974 -0.12676 -0.03565 1.53787 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.45036 1.09324 -7.730 1.08e-14 *** x.var 0.14505 0.01586 9.147 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 254.173 on 74 degrees of freedom Residual deviance: 21.296 on 73 degrees of freedom AIC: 86.11 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- BARBADOS -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.05123 -0.03175 -0.01228 0.00719 0.94772 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0266667 0.0265788 -1.003 0.3190 x.var 0.0010526 0.0006077 1.732 0.0875 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1139 on 73 degrees of freedom Multiple R-squared: 0.03947, Adjusted R-squared: 0.02632 F-statistic: 3 on 1 and 73 DF, p-value: 0.08749 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.03551 -0.02201 -0.00851 0.00499 0.65691 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0184839 0.0184230 -1.003 0.3190 x.var 0.0007296 0.0004213 1.732 0.0875 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07898 on 73 degrees of freedom Multiple R-squared: 0.03947, Adjusted R-squared: 0.02632 F-statistic: 3 on 1 and 73 DF, p-value: 0.08749 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.851e-04 -2.100e-08 -2.100e-08 -2.100e-08 0.000e+00 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1276.38 225638.48 -0.006 0.995 x.var 17.02 3008.51 0.006 0.995 (Dispersion parameter for poisson family taken to be 1) Null deviance: 8.6350e+00 on 74 degrees of freedom Residual deviance: 8.1289e-08 on 73 degrees of freedom AIC: 6 Number of Fisher Scoring iterations: 25 -------------------------------------------------------------------------------- BELARUS -- 8 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.0696 -0.6223 -0.1749 0.2483 6.7853 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.598919 0.269953 -2.219 0.029623 * x.var 0.024182 0.006173 3.918 0.000199 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.157 on 73 degrees of freedom Multiple R-squared: 0.1737, Adjusted R-squared: 0.1624 F-statistic: 15.35 on 1 and 73 DF, p-value: 0.0001994 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.39806 -0.23212 -0.06618 0.09976 1.74535 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.22086 0.09054 -2.439 0.0171 * x.var 0.00897 0.00207 4.333 4.63e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3882 on 73 degrees of freedom Multiple R-squared: 0.2045, Adjusted R-squared: 0.1936 F-statistic: 18.77 on 1 and 73 DF, p-value: 4.632e-05 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.99244 -0.01762 -0.00019 0.00000 1.13388 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -34.4871 7.4127 -4.652 3.28e-06 *** x.var 0.4895 0.1009 4.850 1.23e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 129.0116 on 74 degrees of freedom Residual deviance: 4.5337 on 73 degrees of freedom AIC: 27.098 Number of Fisher Scoring iterations: 9 -------------------------------------------------------------------------------- BELGIUM -- 1447 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -263.27 -172.10 -43.32 93.78 1021.49 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -197.636 58.241 -3.393 0.00112 ** x.var 8.309 1.332 6.239 2.59e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 249.7 on 73 degrees of freedom Multiple R-squared: 0.3478, Adjusted R-squared: 0.3388 F-statistic: 38.93 on 1 and 73 DF, p-value: 2.589e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.53848 -1.27668 0.02115 1.24362 2.36267 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.94087 0.33239 -5.839 1.35e-07 *** x.var 0.09141 0.00760 12.028 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.425 on 73 degrees of freedom Multiple R-squared: 0.6646, Adjusted R-squared: 0.66 F-statistic: 144.7 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -7.7511 -1.5966 -0.2588 -0.0372 4.9676 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.164194 0.167986 -54.55 <2e-16 *** x.var 0.221847 0.002362 93.92 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 32373.86 on 74 degrees of freedom Residual deviance: 317.31 on 73 degrees of freedom AIC: 482.6 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- BENIN -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- BHUTAN -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- BOLIVIA -- 10 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.2960 -1.3379 -0.3561 0.6407 7.3269 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.31423 0.47651 -2.758 0.00734 ** x.var 0.05388 0.01090 4.945 4.71e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.043 on 73 degrees of freedom Multiple R-squared: 0.251, Adjusted R-squared: 0.2407 F-statistic: 24.46 on 1 and 73 DF, p-value: 4.706e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.64717 -0.36721 -0.08725 0.17757 1.64480 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.366735 0.126615 -2.896 0.00498 ** x.var 0.015133 0.002895 5.227 1.57e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5428 on 73 degrees of freedom Multiple R-squared: 0.2723, Adjusted R-squared: 0.2624 F-statistic: 27.32 on 1 and 73 DF, p-value: 1.575e-06 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.48800 -0.11629 -0.00551 -0.00026 1.53981 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -21.96506 3.23428 -6.791 1.11e-11 *** x.var 0.32935 0.04461 7.383 1.55e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 258.876 on 74 degrees of freedom Residual deviance: 16.616 on 73 degrees of freedom AIC: 49.653 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- BOSNIA AND HERZEGOVINA -- 23 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -4.6394 -2.5929 -0.6957 1.5002 16.2481 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.1506 0.9402 -3.351 0.00128 ** x.var 0.1320 0.0215 6.142 3.88e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 4.031 on 73 degrees of freedom Multiple R-squared: 0.3407, Adjusted R-squared: 0.3316 F-statistic: 37.72 on 1 and 73 DF, p-value: 3.882e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.00436 -0.48668 -0.05295 0.38079 1.72597 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.646621 0.154350 -4.189 7.73e-05 *** x.var 0.027983 0.003529 7.929 1.92e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6617 on 73 degrees of freedom Multiple R-squared: 0.4627, Adjusted R-squared: 0.4553 F-statistic: 62.86 on 1 and 73 DF, p-value: 1.917e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.19801 -0.22498 -0.02978 -0.00352 1.21434 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -13.95524 1.39415 -10.01 <2e-16 *** x.var 0.23091 0.01956 11.81 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 530.281 on 74 degrees of freedom Residual deviance: 12.415 on 73 degrees of freedom AIC: 73.955 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- BRAZIL -- 486 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -85.35 -55.05 -13.75 30.21 349.69 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -63.5088 19.0734 -3.330 0.00137 ** x.var 2.6643 0.4361 6.109 4.45e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 81.77 on 73 degrees of freedom Multiple R-squared: 0.3383, Adjusted R-squared: 0.3292 F-statistic: 37.32 on 1 and 73 DF, p-value: 4.445e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.27980 -1.06554 0.01311 1.03492 2.51882 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.541720 0.309248 -4.985 4.04e-06 *** x.var 0.069482 0.007071 9.826 5.35e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.326 on 73 degrees of freedom Multiple R-squared: 0.5695, Adjusted R-squared: 0.5636 F-statistic: 96.55 on 1 and 73 DF, p-value: 5.349e-15 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -3.8770 -0.8392 -0.1279 -0.0156 2.2262 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.729102 0.305683 -35.10 <2e-16 *** x.var 0.227816 0.004292 53.08 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 10515.02 on 74 degrees of freedom Residual deviance: 122.63 on 73 degrees of freedom AIC: 244.63 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- BRUNEI -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.35659 -0.20027 -0.04395 0.11236 0.63496 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.201081 0.063506 -3.166 0.00225 ** x.var 0.008450 0.001452 5.819 1.47e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2722 on 73 degrees of freedom Multiple R-squared: 0.3169, Adjusted R-squared: 0.3075 F-statistic: 33.86 on 1 and 73 DF, p-value: 1.467e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.24717 -0.13882 -0.03047 0.07788 0.44012 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.139379 0.044019 -3.166 0.00225 ** x.var 0.005857 0.001007 5.819 1.47e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1887 on 73 degrees of freedom Multiple R-squared: 0.3169, Adjusted R-squared: 0.3075 F-statistic: 33.86 on 1 and 73 DF, p-value: 1.467e-07 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.69511 -0.12350 -0.01565 -0.00199 0.99935 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -16.14797 5.30253 -3.045 0.00232 ** x.var 0.22314 0.07454 2.994 0.00276 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 38.1647 on 74 degrees of freedom Residual deviance: 5.4862 on 73 degrees of freedom AIC: 27.486 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- BULGARIA -- 20 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -3.2440 -1.9984 -0.7882 1.4833 13.5635 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.77261 0.68593 -4.042 0.00013 *** x.var 0.12279 0.01568 7.829 2.95e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.94 on 73 degrees of freedom Multiple R-squared: 0.4564, Adjusted R-squared: 0.449 F-statistic: 61.29 on 1 and 73 DF, p-value: 2.951e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.92408 -0.33072 -0.00272 0.34279 1.26489 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.688303 0.116723 -5.897 1.07e-07 *** x.var 0.032906 0.002669 12.329 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5004 on 73 degrees of freedom Multiple R-squared: 0.6756, Adjusted R-squared: 0.6711 F-statistic: 152 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.06906 -0.37803 -0.11812 -0.02747 1.07168 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.64289 0.81502 -9.378 <2e-16 *** x.var 0.14093 0.01185 11.890 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 401.111 on 74 degrees of freedom Residual deviance: 15.595 on 73 degrees of freedom AIC: 103.54 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- BURKINA FASO -- 17 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -4.9269 -2.7517 -0.5765 2.0487 9.2228 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.4739 0.8442 -4.115 0.000101 *** x.var 0.1500 0.0193 7.771 3.78e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.619 on 73 degrees of freedom Multiple R-squared: 0.4528, Adjusted R-squared: 0.4453 F-statistic: 60.39 on 1 and 73 DF, p-value: 3.783e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.10652 -0.51303 -0.02009 0.45728 1.23287 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.737118 0.154101 -4.783 8.73e-06 *** x.var 0.032922 0.003524 9.343 4.24e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6606 on 73 degrees of freedom Multiple R-squared: 0.5446, Adjusted R-squared: 0.5384 F-statistic: 87.3 on 1 and 73 DF, p-value: 4.241e-14 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.88350 -0.42505 -0.10500 -0.02392 1.51250 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.42984 0.91537 -10.3 <2e-16 *** x.var 0.16917 0.01311 12.9 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 543.701 on 74 degrees of freedom Residual deviance: 29.517 on 73 degrees of freedom AIC: 103.63 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- CABO VERDE -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.44850 -0.23639 -0.02429 0.18782 0.54004 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.262342 0.067576 -3.882 0.000225 *** x.var 0.011465 0.001545 7.420 1.72e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2897 on 73 degrees of freedom Multiple R-squared: 0.4299, Adjusted R-squared: 0.4221 F-statistic: 55.06 on 1 and 73 DF, p-value: 1.721e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.31087 -0.16385 -0.01683 0.13019 0.37433 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.181842 0.046840 -3.882 0.000225 *** x.var 0.007947 0.001071 7.420 1.72e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2008 on 73 degrees of freedom Multiple R-squared: 0.4299, Adjusted R-squared: 0.4221 F-statistic: 55.06 on 1 and 73 DF, p-value: 1.721e-10 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.70764 -0.21446 -0.05563 -0.01338 1.02250 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.94108 2.96746 -3.687 0.000227 *** x.var 0.15413 0.04282 3.600 0.000319 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 45.5660 on 74 degrees of freedom Residual deviance: 7.9522 on 73 degrees of freedom AIC: 37.952 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- CAMBODIA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- CAMEROON -- 9 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.3322 -1.3473 -0.3625 0.7417 6.0113 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.428108 0.432422 -3.303 0.00149 ** x.var 0.059687 0.009888 6.037 6e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.854 on 73 degrees of freedom Multiple R-squared: 0.333, Adjusted R-squared: 0.3238 F-statistic: 36.44 on 1 and 73 DF, p-value: 5.997e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.73957 -0.39208 -0.06521 0.26534 1.35640 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.44378 0.12334 -3.598 0.00058 *** x.var 0.01878 0.00282 6.660 4.41e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5287 on 73 degrees of freedom Multiple R-squared: 0.378, Adjusted R-squared: 0.3695 F-statistic: 44.36 on 1 and 73 DF, p-value: 4.414e-09 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.28110 -0.19314 -0.02375 -0.00260 1.86415 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -15.35316 2.15867 -7.112 1.14e-12 *** x.var 0.23932 0.03022 7.918 2.41e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 253.132 on 74 degrees of freedom Residual deviance: 15.602 on 73 degrees of freedom AIC: 59.312 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- CANADA -- 259 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -40.296 -27.336 -5.997 15.047 189.193 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -32.018 9.316 -3.437 0.000975 *** x.var 1.358 0.213 6.373 1.48e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 39.94 on 73 degrees of freedom Multiple R-squared: 0.3575, Adjusted R-squared: 0.3487 F-statistic: 40.62 on 1 and 73 DF, p-value: 1.478e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.68351 -0.93228 0.01491 0.80151 2.04811 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.386711 0.237608 -5.836 1.37e-07 *** x.var 0.065324 0.005433 12.023 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.019 on 73 degrees of freedom Multiple R-squared: 0.6645, Adjusted R-squared: 0.6599 F-statistic: 144.6 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.76503 -0.48325 -0.12428 -0.02143 1.52327 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.486657 0.371444 -25.54 <2e-16 *** x.var 0.201001 0.005255 38.25 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 5053.849 on 74 degrees of freedom Residual deviance: 29.285 on 73 degrees of freedom AIC: 162.34 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- CENTRAL AFRICAN REPUBLIC -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- CHAD -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- CHILE -- 34 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -5.7335 -3.3568 -0.8493 1.6787 25.8754 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.83063 1.23193 -3.109 0.00267 ** x.var 0.15940 0.02817 5.659 2.81e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.281 on 73 degrees of freedom Multiple R-squared: 0.3049, Adjusted R-squared: 0.2954 F-statistic: 32.02 on 1 and 73 DF, p-value: 2.811e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.07069 -0.53211 -0.05175 0.40362 2.04797 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.676062 0.165474 -4.086 0.000111 *** x.var 0.029113 0.003784 7.694 5.28e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7094 on 73 degrees of freedom Multiple R-squared: 0.4478, Adjusted R-squared: 0.4403 F-statistic: 59.2 on 1 and 73 DF, p-value: 5.277e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.28378 -0.15747 -0.02185 -0.00208 0.64480 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -15.46884 1.41553 -10.93 <2e-16 *** x.var 0.25459 0.01975 12.89 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 660.1552 on 74 degrees of freedom Residual deviance: 9.9625 on 73 degrees of freedom AIC: 71.472 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- CHINA -- 3333 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -794.98 -374.28 -73.42 427.37 658.50 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 150.212 100.673 1.492 0.14 x.var 53.037 2.302 23.040 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 431.6 on 73 degrees of freedom Multiple R-squared: 0.8791, Adjusted R-squared: 0.8775 F-statistic: 530.8 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.4160 -0.5170 0.1897 0.6470 0.9189 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.255008 0.189641 27.71 <2e-16 *** x.var 0.051328 0.004336 11.84 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.813 on 73 degrees of freedom Multiple R-squared: 0.6575, Adjusted R-squared: 0.6528 F-statistic: 140.1 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -35.879 -16.244 -0.551 14.520 21.129 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 6.5386544 0.0066833 978.4 <2e-16 *** x.var 0.0260027 0.0001254 207.3 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 71263 on 74 degrees of freedom Residual deviance: 24242 on 73 degrees of freedom AIC: 24924 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- GLM using Family Family: Gamma Link function: log : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.04945 -0.52562 -0.03927 0.42442 0.68296 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.844134 0.126156 46.32 <2e-16 *** x.var 0.041986 0.002885 14.55 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for Gamma family taken to be 0.292486) Null deviance: 71.784 on 74 degrees of freedom Residual deviance: 35.655 on 73 degrees of freedom AIC: 1249.3 Number of Fisher Scoring iterations: 11 -------------------------------------------------------------------------------- COLOMBIA -- 35 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -6.690 -3.818 -1.131 2.112 25.530 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -4.4288 1.3516 -3.277 0.00161 ** x.var 0.1853 0.0309 5.997 7.07e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.794 on 73 degrees of freedom Multiple R-squared: 0.33, Adjusted R-squared: 0.3209 F-statistic: 35.96 on 1 and 73 DF, p-value: 7.071e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.16614 -0.58174 -0.02894 0.46069 1.94355 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.72919 0.17405 -4.189 7.73e-05 *** x.var 0.03159 0.00398 7.937 1.85e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7462 on 73 degrees of freedom Multiple R-squared: 0.4632, Adjusted R-squared: 0.4559 F-statistic: 63 on 1 and 73 DF, p-value: 1.847e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.55031 -0.22247 -0.03579 -0.00406 0.98362 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -13.94819 1.20331 -11.59 <2e-16 *** x.var 0.23553 0.01686 13.97 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 749.240 on 74 degrees of freedom Residual deviance: 16.484 on 73 degrees of freedom AIC: 81.676 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- CONGO (BRAZZAVILLE) -- 5 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.5175 -0.3096 -0.1017 0.1062 4.4375 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.280360 0.151884 -1.846 0.06896 . x.var 0.011238 0.003473 3.236 0.00182 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6511 on 73 degrees of freedom Multiple R-squared: 0.1254, Adjusted R-squared: 0.1135 F-statistic: 10.47 on 1 and 73 DF, p-value: 0.001824 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.23837 -0.14277 -0.04716 0.04844 1.53271 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.128543 0.064304 -1.999 0.04933 * x.var 0.005168 0.001470 3.515 0.00076 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2757 on 73 degrees of freedom Multiple R-squared: 0.1447, Adjusted R-squared: 0.133 F-statistic: 12.35 on 1 and 73 DF, p-value: 0.0007603 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.88298 -0.00418 -0.00001 0.00000 1.19699 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -47.1338 14.7581 -3.194 0.00140 ** x.var 0.6506 0.1996 3.259 0.00112 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 66.6432 on 74 degrees of freedom Residual deviance: 3.4997 on 73 degrees of freedom AIC: 18.821 Number of Fisher Scoring iterations: 10 -------------------------------------------------------------------------------- CONGO (KINSHASA) -- 18 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -3.7051 -2.1785 -0.5455 1.3487 12.7151 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.50883 0.74212 -3.381 0.00116 ** x.var 0.10532 0.01697 6.207 2.96e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.181 on 73 degrees of freedom Multiple R-squared: 0.3454, Adjusted R-squared: 0.3365 F-statistic: 38.52 on 1 and 73 DF, p-value: 2.964e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.92556 -0.44853 -0.04886 0.32503 1.63209 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.595781 0.142034 -4.195 7.59e-05 *** x.var 0.025786 0.003248 7.940 1.83e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6089 on 73 degrees of freedom Multiple R-squared: 0.4634, Adjusted R-squared: 0.456 F-statistic: 63.04 on 1 and 73 DF, p-value: 1.828e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.10479 -0.18545 -0.02660 -0.00369 1.08616 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -13.81927 1.52254 -9.076 <2e-16 *** x.var 0.22585 0.02139 10.560 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 419.48 on 74 degrees of freedom Residual deviance: 10.12 on 73 degrees of freedom AIC: 68.882 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- COSTA RICA -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.98577 -0.46734 -0.00497 0.50463 0.93017 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.611532 0.130891 -4.672 1.33e-05 *** x.var 0.028023 0.002993 9.363 3.90e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5611 on 73 degrees of freedom Multiple R-squared: 0.5456, Adjusted R-squared: 0.5394 F-statistic: 87.67 on 1 and 73 DF, p-value: 3.895e-14 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.54851 -0.26063 0.02724 0.27311 0.50342 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.338458 0.072057 -4.697 1.21e-05 *** x.var 0.015561 0.001648 9.444 2.75e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3089 on 73 degrees of freedom Multiple R-squared: 0.5499, Adjusted R-squared: 0.5438 F-statistic: 89.2 on 1 and 73 DF, p-value: 2.746e-14 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.99806 -0.40588 -0.14638 -0.05251 1.34280 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.46665 1.36588 -5.467 4.59e-08 *** x.var 0.11722 0.02023 5.793 6.90e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 98.158 on 74 degrees of freedom Residual deviance: 18.279 on 73 degrees of freedom AIC: 68.098 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- COTE D'IVOIRE -- 3 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.4155 -0.2355 -0.0555 0.1245 2.5067 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.236396 0.091936 -2.571 0.0122 * x.var 0.009730 0.002102 4.628 1.56e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3941 on 73 degrees of freedom Multiple R-squared: 0.2269, Adjusted R-squared: 0.2163 F-statistic: 21.42 on 1 and 73 DF, p-value: 1.563e-05 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.25760 -0.14633 -0.03506 0.07621 1.08058 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.145374 0.051038 -2.848 0.00571 ** x.var 0.006015 0.001167 5.154 2.1e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2188 on 73 degrees of freedom Multiple R-squared: 0.2668, Adjusted R-squared: 0.2567 F-statistic: 26.56 on 1 and 73 DF, p-value: 2.099e-06 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.74412 -0.05532 -0.00376 -0.00022 0.98023 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -21.93604 7.00663 -3.131 0.00174 ** x.var 0.30538 0.09695 3.150 0.00163 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 46.8897 on 74 degrees of freedom Residual deviance: 4.3414 on 73 degrees of freedom AIC: 25.333 Number of Fisher Scoring iterations: 9 -------------------------------------------------------------------------------- CROATIA -- 15 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.5968 -1.6693 -0.2963 1.0174 11.0042 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.83351 0.52487 -3.493 0.000814 *** x.var 0.07772 0.01200 6.476 9.59e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.25 on 73 degrees of freedom Multiple R-squared: 0.3649, Adjusted R-squared: 0.3562 F-statistic: 41.94 on 1 and 73 DF, p-value: 9.592e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.80483 -0.36929 -0.09856 0.33698 1.54400 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.537089 0.121833 -4.408 3.52e-05 *** x.var 0.023542 0.002786 8.451 2.00e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5223 on 73 degrees of freedom Multiple R-squared: 0.4945, Adjusted R-squared: 0.4876 F-statistic: 71.42 on 1 and 73 DF, p-value: 2e-12 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.90217 -0.23160 -0.04848 -0.00752 1.07364 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.39234 1.55805 -7.954 1.81e-15 *** x.var 0.20164 0.02204 9.149 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 297.6360 on 74 degrees of freedom Residual deviance: 9.5981 on 73 degrees of freedom AIC: 68.331 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- DIAMOND PRINCESS -- 11 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.72791 -0.64674 0.08791 0.76117 2.22526 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.402162 0.259531 -9.256 6.18e-14 *** x.var 0.176899 0.005934 29.810 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.113 on 73 degrees of freedom Multiple R-squared: 0.9241, Adjusted R-squared: 0.923 F-statistic: 888.6 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.8408 -0.2414 0.0036 0.2741 0.7057 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.446448 0.086276 -5.175 1.93e-06 *** x.var 0.044387 0.001973 22.500 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3699 on 73 degrees of freedom Multiple R-squared: 0.874, Adjusted R-squared: 0.8722 F-statistic: 506.3 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.8121 -1.1744 -0.2473 0.6183 1.8240 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.941903 0.203522 -4.628 3.69e-06 *** x.var 0.049578 0.003423 14.482 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 364.520 on 74 degrees of freedom Residual deviance: 85.718 on 73 degrees of freedom AIC: 261.36 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- CUBA -- 8 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.7766 -1.0053 -0.2648 0.6186 5.1926 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.261622 0.325959 -3.870 0.000234 *** x.var 0.054253 0.007453 7.279 3.15e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.397 on 73 degrees of freedom Multiple R-squared: 0.4206, Adjusted R-squared: 0.4126 F-statistic: 52.99 on 1 and 73 DF, p-value: 3.153e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.69398 -0.31031 -0.05108 0.24964 1.10920 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.467400 0.100763 -4.639 1.50e-05 *** x.var 0.020739 0.002304 9.001 1.85e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.432 on 73 degrees of freedom Multiple R-squared: 0.526, Adjusted R-squared: 0.5196 F-statistic: 81.02 on 1 and 73 DF, p-value: 1.851e-13 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.83035 -0.23308 -0.05020 -0.00992 0.92189 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.88572 1.58743 -6.857 7.01e-12 *** x.var 0.17537 0.02267 7.735 1.03e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 197.3727 on 74 degrees of freedom Residual deviance: 8.3269 on 73 degrees of freedom AIC: 65.029 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- CYPRUS -- 9 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -3.0534 -1.6734 -0.3044 1.1705 6.8592 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.96505 0.52138 -3.769 0.00033 *** x.var 0.08364 0.01192 7.016 9.73e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.235 on 73 degrees of freedom Multiple R-squared: 0.4027, Adjusted R-squared: 0.3946 F-statistic: 49.22 on 1 and 73 DF, p-value: 9.73e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.89459 -0.44695 -0.04771 0.37927 1.27577 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.557197 0.133168 -4.184 7.88e-05 *** x.var 0.024196 0.003045 7.946 1.78e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5709 on 73 degrees of freedom Multiple R-squared: 0.4638, Adjusted R-squared: 0.4565 F-statistic: 63.15 on 1 and 73 DF, p-value: 1.776e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.90566 -0.27758 -0.05480 -0.00919 1.17610 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.71750 1.42995 -8.194 2.52e-16 *** x.var 0.19319 0.02028 9.524 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 324.038 on 74 degrees of freedom Residual deviance: 19.759 on 73 degrees of freedom AIC: 75.688 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- CZECHIA -- 67 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -12.795 -7.631 -2.111 4.121 48.863 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -8.57297 2.73141 -3.139 0.00245 ** x.var 0.35613 0.06246 5.702 2.36e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.71 on 73 degrees of freedom Multiple R-squared: 0.3082, Adjusted R-squared: 0.2987 F-statistic: 32.52 on 1 and 73 DF, p-value: 2.358e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.36472 -0.72108 -0.08983 0.54765 2.29778 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.863290 0.213570 -4.042 0.00013 *** x.var 0.037134 0.004883 7.604 7.79e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9156 on 73 degrees of freedom Multiple R-squared: 0.442, Adjusted R-squared: 0.4343 F-statistic: 57.82 on 1 and 73 DF, p-value: 7.789e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.08318 -0.31673 -0.02820 -0.00252 1.55473 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -15.14509 0.97466 -15.54 <2e-16 *** x.var 0.26125 0.01358 19.23 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1498.559 on 74 degrees of freedom Residual deviance: 31.108 on 73 degrees of freedom AIC: 101.9 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- DENMARK -- 179 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -34.355 -21.783 -5.211 14.717 120.073 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -26.7903 7.1000 -3.773 0.000325 *** x.var 1.1429 0.1623 7.040 8.78e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 30.44 on 73 degrees of freedom Multiple R-squared: 0.4044, Adjusted R-squared: 0.3962 F-statistic: 49.56 on 1 and 73 DF, p-value: 8.783e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.88155 -0.85745 -0.01344 0.92022 1.88386 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.34592 0.24710 -5.447 6.59e-07 *** x.var 0.06207 0.00565 10.985 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.059 on 73 degrees of freedom Multiple R-squared: 0.6231, Adjusted R-squared: 0.6179 F-statistic: 120.7 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.51551 -0.84032 -0.20269 -0.03536 2.41824 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.796170 0.376924 -23.34 <2e-16 *** x.var 0.188878 0.005355 35.27 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 4189.618 on 74 degrees of freedom Residual deviance: 72.815 on 73 degrees of freedom AIC: 193.05 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- DJIBOUTI -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- DOMINICAN REPUBLIC -- 82 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -16.856 -11.223 -2.210 6.354 55.397 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -12.32216 3.51591 -3.505 0.000785 *** x.var 0.51900 0.08039 6.456 1.05e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 15.07 on 73 degrees of freedom Multiple R-squared: 0.3634, Adjusted R-squared: 0.3547 F-statistic: 41.68 on 1 and 73 DF, p-value: 1.045e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.4864 -0.7305 -0.1120 0.5980 2.0163 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.033223 0.220352 -4.689 1.25e-05 *** x.var 0.045810 0.005038 9.092 1.25e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9446 on 73 degrees of freedom Multiple R-squared: 0.5311, Adjusted R-squared: 0.5246 F-statistic: 82.67 on 1 and 73 DF, p-value: 1.251e-13 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.63163 -0.62818 -0.11187 -0.01508 2.86946 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.579139 0.654980 -17.68 <2e-16 *** x.var 0.216841 0.009223 23.51 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 2043.301 on 74 degrees of freedom Residual deviance: 59.797 on 73 degrees of freedom AIC: 150.65 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- ECUADOR -- 180 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -33.182 -20.902 -5.623 11.753 125.904 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -24.9366 7.1920 -3.467 0.000885 *** x.var 1.0538 0.1644 6.408 1.28e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 30.83 on 73 degrees of freedom Multiple R-squared: 0.36, Adjusted R-squared: 0.3512 F-statistic: 41.06 on 1 and 73 DF, p-value: 1.278e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.77660 -0.84416 -0.06723 0.87962 2.07993 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.288473 0.242806 -5.307 1.15e-06 *** x.var 0.058944 0.005552 10.617 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.041 on 73 degrees of freedom Multiple R-squared: 0.6069, Adjusted R-squared: 0.6015 F-statistic: 112.7 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.35360 -0.56489 -0.11174 -0.01909 2.11811 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.292057 0.440197 -23.38 <2e-16 *** x.var 0.208739 0.006213 33.60 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 4008.733 on 74 degrees of freedom Residual deviance: 45.479 on 73 degrees of freedom AIC: 160.7 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- EGYPT -- 78 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -16.809 -10.892 -2.390 7.286 47.463 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -13.44324 3.16530 -4.247 6.30e-05 *** x.var 0.58640 0.07238 8.102 9.05e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 13.57 on 73 degrees of freedom Multiple R-squared: 0.4735, Adjusted R-squared: 0.4663 F-statistic: 65.64 on 1 and 73 DF, p-value: 9.052e-12 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.41214 -0.75066 -0.01003 0.70266 1.33087 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.167740 0.189446 -6.164 3.54e-08 *** x.var 0.056084 0.004332 12.947 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8121 on 73 degrees of freedom Multiple R-squared: 0.6966, Adjusted R-squared: 0.6925 F-statistic: 167.6 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.8923 -0.5853 -0.2381 -0.0607 1.6827 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.157733 0.421794 -16.97 <2e-16 *** x.var 0.156284 0.006079 25.71 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1974.665 on 74 degrees of freedom Residual deviance: 38.049 on 73 degrees of freedom AIC: 162.67 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- EL SALVADOR -- 3 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.53218 -0.31007 -0.08797 0.13414 2.40780 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.296216 0.125154 -2.367 0.0206 * x.var 0.012006 0.002862 4.195 7.57e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5365 on 73 degrees of freedom Multiple R-squared: 0.1943, Adjusted R-squared: 0.1832 F-statistic: 17.6 on 1 and 73 DF, p-value: 7.572e-05 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.28059 -0.16372 -0.04684 0.07003 1.07412 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.155315 0.062717 -2.476 0.0156 * x.var 0.006317 0.001434 4.405 3.56e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2689 on 73 degrees of freedom Multiple R-squared: 0.21, Adjusted R-squared: 0.1992 F-statistic: 19.41 on 1 and 73 DF, p-value: 3.558e-05 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.78848 -0.01758 -0.00031 -0.00001 0.71930 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -31.2054 9.2717 -3.366 0.000764 *** x.var 0.4353 0.1267 3.437 0.000588 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 62.7105 on 74 degrees of freedom Residual deviance: 3.2397 on 73 degrees of freedom AIC: 22.451 Number of Fisher Scoring iterations: 9 -------------------------------------------------------------------------------- EQUATORIAL GUINEA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- ERITREA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- ESTONIA -- 15 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.6424 -1.6512 -0.3864 0.7416 11.5372 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.66450 0.60583 -2.747 0.00756 ** x.var 0.06836 0.01385 4.935 4.9e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.597 on 73 degrees of freedom Multiple R-squared: 0.2502, Adjusted R-squared: 0.2399 F-statistic: 24.36 on 1 and 73 DF, p-value: 4.896e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.71631 -0.37773 -0.07576 0.22621 1.83667 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.43668 0.12857 -3.396 0.00111 ** x.var 0.01830 0.00294 6.225 2.74e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5512 on 73 degrees of freedom Multiple R-squared: 0.3468, Adjusted R-squared: 0.3378 F-statistic: 38.75 on 1 and 73 DF, p-value: 2.742e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.96766 -0.09321 -0.00566 -0.00030 1.25512 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -20.89965 2.76298 -7.564 3.9e-14 *** x.var 0.31797 0.03816 8.331 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 310.3487 on 74 degrees of freedom Residual deviance: 6.9015 on 73 degrees of freedom AIC: 49.192 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- ESWATINI -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- ETHIOPIA -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.10246 -0.06351 -0.02456 0.01439 1.89544 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.053333 0.053158 -1.003 0.3190 x.var 0.002105 0.001215 1.732 0.0875 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2279 on 73 degrees of freedom Multiple R-squared: 0.03947, Adjusted R-squared: 0.02632 F-statistic: 3 on 1 and 73 DF, p-value: 0.08749 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.05628 -0.03489 -0.01349 0.00790 1.04118 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0292963 0.0291998 -1.003 0.3190 x.var 0.0011564 0.0006677 1.732 0.0875 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1252 on 73 degrees of freedom Multiple R-squared: 0.03947, Adjusted R-squared: 0.02632 F-statistic: 3 on 1 and 73 DF, p-value: 0.08749 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.975e-04 -2.100e-08 -2.100e-08 -2.100e-08 2.100e-08 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1321.29 216238.21 -0.006 0.995 x.var 17.63 2883.18 0.006 0.995 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1.727e+01 on 74 degrees of freedom Residual deviance: 8.851e-08 on 73 degrees of freedom AIC: 6.6137 Number of Fisher Scoring iterations: 25 -------------------------------------------------------------------------------- FIJI -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- FINLAND -- 28 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -5.8887 -3.6288 -0.6994 2.0627 19.4330 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.98775 1.15787 -3.444 0.000953 *** x.var 0.16740 0.02648 6.323 1.83e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 4.964 on 73 degrees of freedom Multiple R-squared: 0.3539, Adjusted R-squared: 0.345 F-statistic: 39.98 on 1 and 73 DF, p-value: 1.826e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.09076 -0.53128 -0.08705 0.44522 1.78988 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.703747 0.170244 -4.134 9.41e-05 *** x.var 0.030415 0.003893 7.813 3.16e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7298 on 73 degrees of freedom Multiple R-squared: 0.4554, Adjusted R-squared: 0.448 F-statistic: 61.05 on 1 and 73 DF, p-value: 3.155e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.39165 -0.33948 -0.04692 -0.00581 1.47174 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -13.36548 1.20846 -11.06 <2e-16 *** x.var 0.22599 0.01697 13.31 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 673.156 on 74 degrees of freedom Residual deviance: 22.355 on 73 degrees of freedom AIC: 86.59 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- FRANCE -- 8093 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1482.1 -969.0 -218.7 534.7 5585.9 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1143.064 318.110 -3.593 0.000589 *** x.var 48.669 7.274 6.691 3.88e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1364 on 73 degrees of freedom Multiple R-squared: 0.3801, Adjusted R-squared: 0.3717 F-statistic: 44.77 on 1 and 73 DF, p-value: 3.875e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.9131 -0.9204 -0.1109 1.1028 2.1311 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.26294 0.26722 -8.468 1.86e-12 *** x.var 0.13187 0.00611 21.581 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.146 on 73 degrees of freedom Multiple R-squared: 0.8645, Adjusted R-squared: 0.8626 F-statistic: 465.8 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -12.6060 -3.3517 -0.5630 -0.1267 9.0302 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -5.3087598 0.0590708 -89.87 <2e-16 *** x.var 0.1925926 0.0008381 229.80 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 177804.39 on 74 degrees of freedom Residual deviance: 991.11 on 73 degrees of freedom AIC: 1286.4 Number of Fisher Scoring iterations: 4 -------------------------------------------------------------------------------- GABON -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.50718 -0.24771 0.01177 0.25931 0.47880 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.306306 0.068157 -4.494 2.57e-05 *** x.var 0.014026 0.001558 9.000 1.86e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2922 on 73 degrees of freedom Multiple R-squared: 0.526, Adjusted R-squared: 0.5195 F-statistic: 81 on 1 and 73 DF, p-value: 1.864e-13 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.35155 -0.17170 0.00816 0.17974 0.33188 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.212315 0.047243 -4.494 2.57e-05 *** x.var 0.009722 0.001080 9.000 1.86e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2025 on 73 degrees of freedom Multiple R-squared: 0.526, Adjusted R-squared: 0.5195 F-statistic: 81 on 1 and 73 DF, p-value: 1.864e-13 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.7147 -0.2959 -0.1088 -0.0389 1.0339 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.18732 1.93881 -4.223 2.41e-05 *** x.var 0.11763 0.02871 4.097 4.18e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 50.465 on 74 degrees of freedom Residual deviance: 10.408 on 73 degrees of freedom AIC: 48.408 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- GAMBIA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.4661 -0.2413 -0.0166 0.2081 0.5218 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.274955 0.067979 -4.045 0.000129 *** x.var 0.012148 0.001554 7.815 3.13e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2914 on 73 degrees of freedom Multiple R-squared: 0.4555, Adjusted R-squared: 0.4481 F-statistic: 61.08 on 1 and 73 DF, p-value: 3.129e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.3231 -0.1673 -0.0115 0.1443 0.3617 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.190584 0.047119 -4.045 0.000129 *** x.var 0.008420 0.001077 7.815 3.13e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.202 on 73 degrees of freedom Multiple R-squared: 0.4555, Adjusted R-squared: 0.4481 F-statistic: 61.08 on 1 and 73 DF, p-value: 3.129e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.70970 -0.23432 -0.06697 -0.01916 1.02614 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.10608 2.63831 -3.831 0.000128 *** x.var 0.14307 0.03832 3.734 0.000189 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 46.9961 on 74 degrees of freedom Residual deviance: 8.5674 on 73 degrees of freedom AIC: 40.567 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- GEORGIA -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.14953 -0.09164 -0.03374 0.02415 1.84421 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.078919 0.058146 -1.357 0.1789 x.var 0.003129 0.001330 2.354 0.0213 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2493 on 73 degrees of freedom Multiple R-squared: 0.07054, Adjusted R-squared: 0.05781 F-statistic: 5.54 on 1 and 73 DF, p-value: 0.02128 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.08921 -0.05468 -0.02016 0.01437 1.00567 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0470309 0.0336842 -1.396 0.1669 x.var 0.0018663 0.0007702 2.423 0.0179 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1444 on 73 degrees of freedom Multiple R-squared: 0.07445, Adjusted R-squared: 0.06177 F-statistic: 5.872 on 1 and 73 DF, p-value: 0.01787 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.5303 0.0000 0.0000 0.0000 0.5251 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -103.161 64.665 -1.595 0.111 x.var 1.386 0.866 1.601 0.109 (Dispersion parameter for poisson family taken to be 1) Null deviance: 22.0858 on 74 degrees of freedom Residual deviance: 0.6796 on 73 degrees of freedom AIC: 9.2933 Number of Fisher Scoring iterations: 12 -------------------------------------------------------------------------------- GERMANY -- 1584 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -288.43 -197.78 -50.65 111.44 1096.71 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -224.624 64.138 -3.502 0.000791 *** x.var 9.492 1.467 6.473 9.75e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 275 on 73 degrees of freedom Multiple R-squared: 0.3646, Adjusted R-squared: 0.3559 F-statistic: 41.89 on 1 and 73 DF, p-value: 9.745e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.55719 -1.14587 0.07078 1.19011 2.09069 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.017434 0.322028 -6.265 2.33e-08 *** x.var 0.097333 0.007363 13.219 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.381 on 73 degrees of freedom Multiple R-squared: 0.7053, Adjusted R-squared: 0.7013 F-statistic: 174.7 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -7.9680 -1.6958 -0.3359 -0.0542 3.9944 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.09297 0.14666 -55.18 <2e-16 *** x.var 0.20873 0.00207 100.84 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 35989.74 on 74 degrees of freedom Residual deviance: 290.32 on 73 degrees of freedom AIC: 470.3 Number of Fisher Scoring iterations: 4 -------------------------------------------------------------------------------- GHANA -- 5 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.0062 -1.0619 -0.1728 0.8538 2.5495 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.270270 0.301164 -4.218 6.99e-05 *** x.var 0.055533 0.006886 8.064 1.07e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.291 on 73 degrees of freedom Multiple R-squared: 0.4711, Adjusted R-squared: 0.4639 F-statistic: 65.03 on 1 and 73 DF, p-value: 1.066e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.77378 -0.38034 -0.02943 0.32148 0.84784 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.480983 0.109594 -4.389 3.78e-05 *** x.var 0.021267 0.002506 8.487 1.71e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4698 on 73 degrees of freedom Multiple R-squared: 0.4966, Adjusted R-squared: 0.4897 F-statistic: 72.03 on 1 and 73 DF, p-value: 1.713e-12 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.45028 -0.37647 -0.09798 -0.02554 1.53985 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.3369 1.3444 -6.945 3.79e-12 *** x.var 0.1538 0.0194 7.924 2.29e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 205.63 on 74 degrees of freedom Residual deviance: 23.66 on 73 degrees of freedom AIC: 78.01 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- GREECE -- 73 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -15.587 -10.037 -1.779 6.817 43.314 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -13.02883 3.01269 -4.325 4.77e-05 *** x.var 0.56953 0.06889 8.268 4.42e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 12.92 on 73 degrees of freedom Multiple R-squared: 0.4836, Adjusted R-squared: 0.4765 F-statistic: 68.35 on 1 and 73 DF, p-value: 4.422e-12 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.55570 -0.69337 -0.05358 0.73529 1.30188 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.170370 0.196208 -5.965 8.06e-08 *** x.var 0.055634 0.004486 12.401 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8411 on 73 degrees of freedom Multiple R-squared: 0.6781, Adjusted R-squared: 0.6737 F-statistic: 153.8 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.06921 -0.68328 -0.23004 -0.06468 1.35460 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.017437 0.420201 -16.70 <2e-16 *** x.var 0.153876 0.006064 25.38 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1915.478 on 74 degrees of freedom Residual deviance: 48.519 on 73 degrees of freedom AIC: 167.89 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- GUATEMALA -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.59799 -0.26115 0.05749 0.22975 1.03786 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.385225 0.078627 -4.899 5.61e-06 *** x.var 0.018208 0.001798 10.128 1.48e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3371 on 73 degrees of freedom Multiple R-squared: 0.5842, Adjusted R-squared: 0.5785 F-statistic: 102.6 on 1 and 73 DF, p-value: 1.481e-15 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.39726 -0.17484 0.04759 0.16573 0.46089 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.251986 0.049692 -5.071 2.9e-06 *** x.var 0.012023 0.001136 10.581 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.213 on 73 degrees of freedom Multiple R-squared: 0.6053, Adjusted R-squared: 0.5999 F-statistic: 112 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.71917 -0.32353 -0.14545 -0.05902 1.03542 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -6.9205 1.4499 -4.773 1.81e-06 *** x.var 0.1031 0.0218 4.730 2.24e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 59.917 on 74 degrees of freedom Residual deviance: 11.666 on 73 degrees of freedom AIC: 58.893 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- GUINEA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- GUYANA -- 4 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.9010 -0.4169 -0.1232 0.2579 2.4005 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.686486 0.162183 -4.233 6.63e-05 *** x.var 0.031750 0.003708 8.562 1.24e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6953 on 73 degrees of freedom Multiple R-squared: 0.501, Adjusted R-squared: 0.4942 F-statistic: 73.3 on 1 and 73 DF, p-value: 1.239e-12 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.49205 -0.18168 -0.00021 0.15650 0.74831 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.346768 0.065020 -5.333 1.04e-06 *** x.var 0.016776 0.001487 11.284 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2787 on 73 degrees of freedom Multiple R-squared: 0.6356, Adjusted R-squared: 0.6306 F-statistic: 127.3 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.85436 -0.33071 -0.13827 -0.05469 1.05386 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.01202 1.22026 -5.746 9.12e-09 *** x.var 0.11247 0.01816 6.193 5.89e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 100.913 on 74 degrees of freedom Residual deviance: 12.468 on 73 degrees of freedom AIC: 72.758 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- HAITI -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.05123 -0.03175 -0.01228 0.00719 0.94772 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0266667 0.0265788 -1.003 0.3190 x.var 0.0010526 0.0006077 1.732 0.0875 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1139 on 73 degrees of freedom Multiple R-squared: 0.03947, Adjusted R-squared: 0.02632 F-statistic: 3 on 1 and 73 DF, p-value: 0.08749 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.03551 -0.02201 -0.00851 0.00499 0.65691 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0184839 0.0184230 -1.003 0.3190 x.var 0.0007296 0.0004213 1.732 0.0875 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.07898 on 73 degrees of freedom Multiple R-squared: 0.03947, Adjusted R-squared: 0.02632 F-statistic: 3 on 1 and 73 DF, p-value: 0.08749 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.851e-04 -2.100e-08 -2.100e-08 -2.100e-08 0.000e+00 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1276.38 225638.48 -0.006 0.995 x.var 17.02 3008.51 0.006 0.995 (Dispersion parameter for poisson family taken to be 1) Null deviance: 8.6350e+00 on 74 degrees of freedom Residual deviance: 8.1289e-08 on 73 degrees of freedom AIC: 6 Number of Fisher Scoring iterations: 25 -------------------------------------------------------------------------------- HOLY SEE -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- HONDURAS -- 22 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -3.7210 -2.2658 -0.6228 1.1140 17.2463 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.28757 0.82238 -2.782 0.00688 ** x.var 0.09388 0.01880 4.993 3.92e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.525 on 73 degrees of freedom Multiple R-squared: 0.2545, Adjusted R-squared: 0.2443 F-statistic: 24.93 on 1 and 73 DF, p-value: 3.923e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.8143 -0.4380 -0.1228 0.2535 2.0975 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.487370 0.148661 -3.278 0.0016 ** x.var 0.020338 0.003399 5.983 7.47e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6373 on 73 degrees of freedom Multiple R-squared: 0.329, Adjusted R-squared: 0.3198 F-statistic: 35.8 on 1 and 73 DF, p-value: 7.473e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.23314 -0.11916 -0.00705 -0.00036 1.45663 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -20.93151 2.39678 -8.733 <2e-16 *** x.var 0.32277 0.03309 9.756 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 432.114 on 74 degrees of freedom Residual deviance: 13.102 on 73 degrees of freedom AIC: 56.406 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- HUNGARY -- 34 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -6.8903 -4.1528 -0.4153 2.6426 22.2589 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -5.24757 1.31327 -3.996 0.000152 *** x.var 0.22651 0.03003 7.543 1.01e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.63 on 73 degrees of freedom Multiple R-squared: 0.438, Adjusted R-squared: 0.4303 F-statistic: 56.9 on 1 and 73 DF, p-value: 1.012e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.21799 -0.61631 -0.00132 0.60701 1.47391 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.862122 0.166384 -5.182 1.88e-06 *** x.var 0.039247 0.003804 10.316 6.65e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7133 on 73 degrees of freedom Multiple R-squared: 0.5931, Adjusted R-squared: 0.5876 F-statistic: 106.4 on 1 and 73 DF, p-value: 6.653e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.37087 -0.43627 -0.12731 -0.02885 1.54164 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.05927 0.74795 -12.11 <2e-16 *** x.var 0.16975 0.01071 15.85 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 801.439 on 74 degrees of freedom Residual deviance: 23.803 on 73 degrees of freedom AIC: 111.41 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- ICELAND -- 4 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.3743 -0.5447 -0.0838 0.3816 3.8101 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.801081 0.204703 -3.913 0.000202 *** x.var 0.036871 0.004681 7.877 2.39e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8775 on 73 degrees of freedom Multiple R-squared: 0.4595, Adjusted R-squared: 0.4521 F-statistic: 62.05 on 1 and 73 DF, p-value: 2.394e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.6742 -0.2701 0.0083 0.2433 1.2074 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.38546 0.08354 -4.614 1.65e-05 *** x.var 0.01796 0.00191 9.402 3.30e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3581 on 73 degrees of freedom Multiple R-squared: 0.5477, Adjusted R-squared: 0.5415 F-statistic: 88.4 on 1 and 73 DF, p-value: 3.297e-14 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.2467 -0.4044 -0.1576 -0.0611 3.9032 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.03055 1.16208 -6.050 1.45e-09 *** x.var 0.11489 0.01725 6.659 2.75e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 131.383 on 74 degrees of freedom Residual deviance: 27.443 on 73 degrees of freedom AIC: 83.51 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- INDIA -- 99 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -16.600 -11.646 -3.090 6.132 69.927 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -13.29153 3.81676 -3.482 0.000843 *** x.var 0.56486 0.08727 6.472 9.75e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 16.36 on 73 degrees of freedom Multiple R-squared: 0.3646, Adjusted R-squared: 0.3559 F-statistic: 41.89 on 1 and 73 DF, p-value: 9.748e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.4288 -0.6207 -0.0829 0.6677 1.8305 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.107710 0.194035 -5.709 2.3e-07 *** x.var 0.051766 0.004437 11.668 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8318 on 73 degrees of freedom Multiple R-squared: 0.6509, Adjusted R-squared: 0.6462 F-statistic: 136.1 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.15182 -0.28877 -0.08975 -0.01462 1.41743 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.030007 0.560512 -17.89 <2e-16 *** x.var 0.196317 0.007942 24.72 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 2085.392 on 74 degrees of freedom Residual deviance: 16.067 on 73 degrees of freedom AIC: 128.39 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- INDONESIA -- 198 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -50.101 -28.284 -6.841 20.618 111.280 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -38.0912 8.7334 -4.362 4.17e-05 *** x.var 1.6642 0.1997 8.334 3.32e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 37.44 on 73 degrees of freedom Multiple R-squared: 0.4875, Adjusted R-squared: 0.4805 F-statistic: 69.45 on 1 and 73 DF, p-value: 3.324e-12 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.0465 -0.8448 0.1385 0.9652 1.4495 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.522361 0.255289 -5.963 8.11e-08 *** x.var 0.072834 0.005837 12.477 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.094 on 73 degrees of freedom Multiple R-squared: 0.6808, Adjusted R-squared: 0.6764 F-statistic: 155.7 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -4.6019 -1.4514 -0.3873 -0.0928 2.8151 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -5.989164 0.247004 -24.25 <2e-16 *** x.var 0.154501 0.003563 43.36 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 5652.45 on 74 degrees of freedom Residual deviance: 186.61 on 73 degrees of freedom AIC: 329.7 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- IRAN -- 3603 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -855.89 -560.75 -34.86 478.37 1434.82 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -850.667 145.503 -5.846 1.31e-07 *** x.var 40.251 3.327 12.098 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 623.8 on 73 degrees of freedom Multiple R-squared: 0.6672, Adjusted R-squared: 0.6627 F-statistic: 146.4 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.1082 -0.5087 0.1885 0.6110 1.7838 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.927957 0.198593 -9.708 8.86e-15 *** x.var 0.144150 0.004541 31.745 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8513 on 73 degrees of freedom Multiple R-squared: 0.9325, Adjusted R-squared: 0.9315 F-statistic: 1008 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -20.4194 -7.9522 -4.5792 -0.5579 14.1679 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.8095196 0.0306856 26.38 <2e-16 *** x.var 0.1026926 0.0004615 222.50 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 111263.6 on 74 degrees of freedom Residual deviance: 4852.5 on 73 degrees of freedom AIC: 5207.7 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- IRAQ -- 61 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -13.010 -10.150 -2.259 8.152 27.121 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -13.55135 2.48689 -5.449 6.53e-07 *** x.var 0.63240 0.05686 11.121 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 10.66 on 73 degrees of freedom Multiple R-squared: 0.6288, Adjusted R-squared: 0.6238 F-statistic: 123.7 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.5123 -0.4490 0.1124 0.4991 1.1218 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.186076 0.155389 -7.633 6.88e-11 *** x.var 0.064246 0.003553 18.082 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6661 on 73 degrees of freedom Multiple R-squared: 0.8175, Adjusted R-squared: 0.815 F-statistic: 327 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.2941 -0.9191 -0.3759 0.3562 1.6506 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -3.732979 0.261203 -14.29 <2e-16 *** x.var 0.108318 0.003904 27.75 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1796.811 on 74 degrees of freedom Residual deviance: 72.289 on 73 degrees of freedom AIC: 228.51 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- IRELAND -- 158 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -27.482 -18.248 -4.326 9.595 114.862 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -20.1416 6.2096 -3.244 0.00178 ** x.var 0.8437 0.1420 5.942 8.84e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 26.62 on 73 degrees of freedom Multiple R-squared: 0.326, Adjusted R-squared: 0.3168 F-statistic: 35.31 on 1 and 73 DF, p-value: 8.842e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.4631 -0.7256 -0.1731 0.6798 2.2083 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.170683 0.221185 -5.293 1.22e-06 *** x.var 0.053750 0.005057 10.628 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9482 on 73 degrees of freedom Multiple R-squared: 0.6074, Adjusted R-squared: 0.602 F-statistic: 112.9 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.05251 -0.32269 -0.05167 -0.00604 1.64621 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.190764 0.554892 -21.97 <2e-16 *** x.var 0.232164 0.007782 29.83 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 3346.980 on 74 degrees of freedom Residual deviance: 30.355 on 73 degrees of freedom AIC: 142.93 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- ISRAEL -- 49 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -9.612 -5.878 -1.111 3.139 35.000 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -6.56721 2.01039 -3.267 0.00166 ** x.var 0.27422 0.04597 5.965 8.04e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 8.618 on 73 degrees of freedom Multiple R-squared: 0.3277, Adjusted R-squared: 0.3185 F-statistic: 35.59 on 1 and 73 DF, p-value: 8.04e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.27226 -0.65975 -0.06671 0.51834 2.07245 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.81972 0.19680 -4.165 8.42e-05 *** x.var 0.03546 0.00450 7.880 2.37e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8436 on 73 degrees of freedom Multiple R-squared: 0.4596, Adjusted R-squared: 0.4522 F-statistic: 62.09 on 1 and 73 DF, p-value: 2.369e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.73761 -0.31329 -0.03753 -0.00400 1.70335 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -14.04331 1.02123 -13.75 <2e-16 *** x.var 0.24231 0.01429 16.96 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1122.292 on 74 degrees of freedom Residual deviance: 25.524 on 73 degrees of freedom AIC: 96.046 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- ITALY -- 15887 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -3754.2 -2528.7 -314.9 1976.2 7513.6 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3477.34 679.96 -5.114 2.45e-06 *** x.var 158.01 15.55 10.163 1.27e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2915 on 73 degrees of freedom Multiple R-squared: 0.5859, Adjusted R-squared: 0.5802 F-statistic: 103.3 on 1 and 73 DF, p-value: 1.274e-15 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.5508 -0.7520 0.2026 0.8246 2.3465 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.515392 0.262945 -9.566 1.63e-14 *** x.var 0.168873 0.006012 28.088 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.127 on 73 degrees of freedom Multiple R-squared: 0.9153, Adjusted R-squared: 0.9141 F-statistic: 788.9 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -41.964 -14.113 -6.439 -1.594 23.862 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.8347625 0.0191039 43.7 <2e-16 *** x.var 0.1220524 0.0002818 433.1 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 475417 on 74 degrees of freedom Residual deviance: 14999 on 73 degrees of freedom AIC: 15377 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- JAMAICA -- 3 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.83982 -0.38561 -0.05416 0.20364 1.81645 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.559640 0.140170 -3.993 0.000154 *** x.var 0.024552 0.003205 7.660 6.11e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6009 on 73 degrees of freedom Multiple R-squared: 0.4456, Adjusted R-squared: 0.438 F-statistic: 58.68 on 1 and 73 DF, p-value: 6.11e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.47034 -0.21935 0.03164 0.16114 0.72601 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.302986 0.066855 -4.532 2.24e-05 *** x.var 0.013567 0.001529 8.875 3.19e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2866 on 73 degrees of freedom Multiple R-squared: 0.519, Adjusted R-squared: 0.5124 F-statistic: 78.77 on 1 and 73 DF, p-value: 3.193e-13 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.72408 -0.24424 -0.07081 -0.01770 0.99341 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.8884 1.9633 -5.036 4.74e-07 *** x.var 0.1500 0.0284 5.282 1.28e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 88.1343 on 74 degrees of freedom Residual deviance: 8.6404 on 73 degrees of freedom AIC: 53.6 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- JAPAN -- 77 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -17.692 -10.106 -1.124 8.620 29.871 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -17.10486 2.59896 -6.581 6.16e-09 *** x.var 0.86802 0.05943 14.607 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.14 on 73 degrees of freedom Multiple R-squared: 0.7451, Adjusted R-squared: 0.7416 F-statistic: 213.4 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.84208 -0.24511 0.00227 0.26174 0.86110 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.931581 0.087089 -10.70 <2e-16 *** x.var 0.070480 0.001991 35.39 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3733 on 73 degrees of freedom Multiple R-squared: 0.9449, Adjusted R-squared: 0.9442 F-statistic: 1253 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.0444 -1.0724 -0.6905 0.2522 1.9782 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.682178 0.162201 -10.37 <2e-16 *** x.var 0.083182 0.002509 33.16 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 2100.020 on 74 degrees of freedom Residual deviance: 88.099 on 73 degrees of freedom AIC: 310.98 Number of Fisher Scoring iterations: 4 -------------------------------------------------------------------------------- JORDAN -- 5 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.5603 -0.8566 -0.2290 0.4746 3.2875 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.91207 0.29126 -3.131 0.0025 ** x.var 0.03804 0.00666 5.711 2.27e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.249 on 73 degrees of freedom Multiple R-squared: 0.3088, Adjusted R-squared: 0.2994 F-statistic: 32.62 on 1 and 73 DF, p-value: 2.272e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.59406 -0.32693 -0.05981 0.17844 1.13995 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.34449 0.10539 -3.269 0.00165 ** x.var 0.01444 0.00241 5.992 7.21e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4518 on 73 degrees of freedom Multiple R-squared: 0.3297, Adjusted R-squared: 0.3205 F-statistic: 35.9 on 1 and 73 DF, p-value: 7.207e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.37768 -0.18049 -0.02081 -0.00213 1.78642 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -16.32984 2.79682 -5.839 5.26e-09 *** x.var 0.24664 0.03909 6.309 2.81e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 169.54 on 74 degrees of freedom Residual deviance: 16.33 on 73 degrees of freedom AIC: 51.686 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- KAZAKHSTAN -- 6 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.2426 -0.6406 -0.2239 0.3471 4.4796 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.732973 0.251696 -2.912 0.00476 ** x.var 0.030868 0.005755 5.364 9.19e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.079 on 73 degrees of freedom Multiple R-squared: 0.2827, Adjusted R-squared: 0.2728 F-statistic: 28.77 on 1 and 73 DF, p-value: 9.186e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.53402 -0.27757 -0.03428 0.14791 1.29353 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.30766 0.09182 -3.350 0.00128 ** x.var 0.01315 0.00210 6.264 2.34e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3936 on 73 degrees of freedom Multiple R-squared: 0.3496, Adjusted R-squared: 0.3407 F-statistic: 39.23 on 1 and 73 DF, p-value: 2.337e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.0869 -0.1840 -0.0375 -0.0050 3.2660 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -14.43015 2.69134 -5.362 8.24e-08 *** x.var 0.21724 0.03789 5.733 9.85e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 135.829 on 74 degrees of freedom Residual deviance: 17.769 on 73 degrees of freedom AIC: 54.153 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- KENYA -- 4 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.8437 -0.4521 -0.0605 0.1829 2.9658 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.510991 0.176513 -2.895 0.005 ** x.var 0.021166 0.004036 5.244 1.47e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7567 on 73 degrees of freedom Multiple R-squared: 0.2737, Adjusted R-squared: 0.2637 F-statistic: 27.5 on 1 and 73 DF, p-value: 1.471e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.41866 -0.22576 -0.03286 0.16005 1.09693 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.248676 0.075273 -3.304 0.00148 ** x.var 0.010427 0.001721 6.058 5.48e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3227 on 73 degrees of freedom Multiple R-squared: 0.3346, Adjusted R-squared: 0.3254 F-statistic: 36.7 on 1 and 73 DF, p-value: 5.484e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.72152 -0.08698 -0.00794 -0.00073 0.91076 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -18.83670 4.20303 -4.482 7.41e-06 *** x.var 0.27329 0.05845 4.676 2.93e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 93.8263 on 74 degrees of freedom Residual deviance: 5.0708 on 73 degrees of freedom AIC: 35.86 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- KOREA, SOUTH -- 183 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -35.32 -21.53 -5.36 22.07 45.05 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -45.5474 5.8094 -7.84 2.81e-11 *** x.var 2.4467 0.1328 18.42 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 24.9 on 73 degrees of freedom Multiple R-squared: 0.8229, Adjusted R-squared: 0.8205 F-statistic: 339.3 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.5527 -0.4281 0.1789 0.4994 1.0546 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.147701 0.147797 -7.765 3.88e-11 *** x.var 0.093118 0.003379 27.554 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6336 on 73 degrees of freedom Multiple R-squared: 0.9123, Adjusted R-squared: 0.9111 F-statistic: 759.2 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -4.619 -2.627 -1.487 1.587 3.182 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.026741 0.083502 0.32 0.749 x.var 0.073411 0.001316 55.80 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 5642.2 on 74 degrees of freedom Residual deviance: 433.9 on 73 degrees of freedom AIC: 699.59 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- KUWAIT -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.09936 -0.06093 -0.02251 0.01591 0.89857 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0522523 0.0365793 -1.428 0.1574 x.var 0.0020768 0.0008364 2.483 0.0153 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1568 on 73 degrees of freedom Multiple R-squared: 0.07788, Adjusted R-squared: 0.06525 F-statistic: 6.165 on 1 and 73 DF, p-value: 0.01532 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.06887 -0.04224 -0.01560 0.01103 0.62284 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0362185 0.0253548 -1.428 0.1574 x.var 0.0014395 0.0005798 2.483 0.0153 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1087 on 73 degrees of freedom Multiple R-squared: 0.07788, Adjusted R-squared: 0.06525 F-statistic: 6.165 on 1 and 73 DF, p-value: 0.01532 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.54433 -0.00004 0.00000 0.00000 0.71467 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -82.1082 60.8327 -1.350 0.177 x.var 1.0986 0.8165 1.346 0.178 (Dispersion parameter for poisson family taken to be 1) Null deviance: 14.4974 on 74 degrees of freedom Residual deviance: 1.0465 on 73 degrees of freedom AIC: 9.0465 Number of Fisher Scoring iterations: 12 -------------------------------------------------------------------------------- KYRGYZSTAN -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.14447 -0.08762 -0.03078 0.02606 0.85246 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0767568 0.0435822 -1.761 0.08239 . x.var 0.0030725 0.0009965 3.083 0.00289 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1868 on 73 degrees of freedom Multiple R-squared: 0.1152, Adjusted R-squared: 0.1031 F-statistic: 9.506 on 1 and 73 DF, p-value: 0.002889 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.10014 -0.06074 -0.02134 0.01806 0.59088 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0532037 0.0302089 -1.761 0.08239 . x.var 0.0021297 0.0006907 3.083 0.00289 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1295 on 73 degrees of freedom Multiple R-squared: 0.1152, Adjusted R-squared: 0.1031 F-statistic: 9.506 on 1 and 73 DF, p-value: 0.002889 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.61237 -0.00144 0.00000 0.00000 0.84360 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -51.5806 30.2159 -1.707 0.0878 . x.var 0.6931 0.4082 1.698 0.0895 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 19.3133 on 74 degrees of freedom Residual deviance: 1.7261 on 73 degrees of freedom AIC: 11.726 Number of Fisher Scoring iterations: 11 -------------------------------------------------------------------------------- LATVIA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.14447 -0.08762 -0.03078 0.02606 0.85246 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0767568 0.0435822 -1.761 0.08239 . x.var 0.0030725 0.0009965 3.083 0.00289 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1868 on 73 degrees of freedom Multiple R-squared: 0.1152, Adjusted R-squared: 0.1031 F-statistic: 9.506 on 1 and 73 DF, p-value: 0.002889 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.10014 -0.06074 -0.02134 0.01806 0.59088 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0532037 0.0302089 -1.761 0.08239 . x.var 0.0021297 0.0006907 3.083 0.00289 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1295 on 73 degrees of freedom Multiple R-squared: 0.1152, Adjusted R-squared: 0.1031 F-statistic: 9.506 on 1 and 73 DF, p-value: 0.002889 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.61237 -0.00144 0.00000 0.00000 0.84360 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -51.5806 30.2159 -1.707 0.0878 . x.var 0.6931 0.4082 1.698 0.0895 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 19.3133 on 74 degrees of freedom Residual deviance: 1.7261 on 73 degrees of freedom AIC: 11.726 Number of Fisher Scoring iterations: 11 -------------------------------------------------------------------------------- LEBANON -- 18 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -4.1535 -2.2331 -0.8072 1.6627 9.5442 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.49514 0.72440 -4.825 7.46e-06 *** x.var 0.15935 0.01656 9.620 1.29e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.105 on 73 degrees of freedom Multiple R-squared: 0.559, Adjusted R-squared: 0.553 F-statistic: 92.55 on 1 and 73 DF, p-value: 1.292e-14 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.07167 -0.39653 0.07481 0.39345 0.85421 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.780155 0.122917 -6.347 1.65e-08 *** x.var 0.038580 0.002811 13.727 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5269 on 73 degrees of freedom Multiple R-squared: 0.7208, Adjusted R-squared: 0.7169 F-statistic: 188.4 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.30307 -0.51158 -0.19077 0.00077 1.51387 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -5.927868 0.589849 -10.05 <2e-16 *** x.var 0.120087 0.008715 13.78 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 485.629 on 74 degrees of freedom Residual deviance: 25.376 on 73 degrees of freedom AIC: 126.05 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- LIBERIA -- 3 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.19971 -0.12234 -0.04497 0.03240 2.79193 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.105586 0.082685 -1.277 0.2057 x.var 0.004182 0.001891 2.212 0.0301 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3545 on 73 degrees of freedom Multiple R-squared: 0.06282, Adjusted R-squared: 0.04998 F-statistic: 4.893 on 1 and 73 DF, p-value: 0.0301 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.10365 -0.06352 -0.02339 0.01674 1.27831 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0547024 0.0403038 -1.357 0.1789 x.var 0.0021692 0.0009216 2.354 0.0213 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1728 on 73 degrees of freedom Multiple R-squared: 0.07054, Adjusted R-squared: 0.05781 F-statistic: 5.54 on 1 and 73 DF, p-value: 0.02128 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.5060 0.0000 0.0000 0.0000 0.4154 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -119.5447 66.8573 -1.788 0.0738 . x.var 1.6094 0.8944 1.799 0.0719 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 30.04122 on 74 degrees of freedom Residual deviance: 0.50534 on 73 degrees of freedom AIC: 9.4972 Number of Fisher Scoring iterations: 12 -------------------------------------------------------------------------------- LIECHTENSTEIN -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.09936 -0.06093 -0.02251 0.01591 0.89857 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0522523 0.0365793 -1.428 0.1574 x.var 0.0020768 0.0008364 2.483 0.0153 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1568 on 73 degrees of freedom Multiple R-squared: 0.07788, Adjusted R-squared: 0.06525 F-statistic: 6.165 on 1 and 73 DF, p-value: 0.01532 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.06887 -0.04224 -0.01560 0.01103 0.62284 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0362185 0.0253548 -1.428 0.1574 x.var 0.0014395 0.0005798 2.483 0.0153 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1087 on 73 degrees of freedom Multiple R-squared: 0.07788, Adjusted R-squared: 0.06525 F-statistic: 6.165 on 1 and 73 DF, p-value: 0.01532 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.54433 -0.00004 0.00000 0.00000 0.71467 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -82.1082 60.8327 -1.350 0.177 x.var 1.0986 0.8165 1.346 0.178 (Dispersion parameter for poisson family taken to be 1) Null deviance: 14.4974 on 74 degrees of freedom Residual deviance: 1.0465 on 73 degrees of freedom AIC: 9.0465 Number of Fisher Scoring iterations: 12 -------------------------------------------------------------------------------- LITHUANIA -- 13 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -3.1567 -1.7814 -0.2285 1.2067 8.4235 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.07856 0.53833 -3.861 0.000242 *** x.var 0.08873 0.01231 7.209 4.27e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.308 on 73 degrees of freedom Multiple R-squared: 0.4158, Adjusted R-squared: 0.4078 F-statistic: 51.97 on 1 and 73 DF, p-value: 4.265e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.91043 -0.44374 -0.05273 0.38873 1.32500 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.577932 0.134234 -4.305 5.11e-05 *** x.var 0.025226 0.003069 8.219 5.46e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5754 on 73 degrees of freedom Multiple R-squared: 0.4806, Adjusted R-squared: 0.4735 F-statistic: 67.55 on 1 and 73 DF, p-value: 5.461e-12 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.28416 -0.32986 -0.07008 -0.01237 1.52575 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.26279 1.34236 -8.390 <2e-16 *** x.var 0.18762 0.01908 9.834 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 337.959 on 74 degrees of freedom Residual deviance: 19.271 on 73 degrees of freedom AIC: 77.97 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- LUXEMBOURG -- 36 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -8.359 -4.963 -1.417 3.548 21.251 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -6.52613 1.54871 -4.214 7.09e-05 *** x.var 0.28367 0.03541 8.011 1.35e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 6.639 on 73 degrees of freedom Multiple R-squared: 0.4678, Adjusted R-squared: 0.4605 F-statistic: 64.17 on 1 and 73 DF, p-value: 1.345e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.31115 -0.66152 0.03095 0.54510 1.30402 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.94011 0.17450 -5.387 8.35e-07 *** x.var 0.04329 0.00399 10.850 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7481 on 73 degrees of freedom Multiple R-squared: 0.6173, Adjusted R-squared: 0.612 F-statistic: 117.7 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.7168 -0.5348 -0.1623 -0.0368 1.6141 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.1819 0.6259 -13.07 <2e-16 *** x.var 0.1605 0.0090 17.83 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 983.830 on 74 degrees of freedom Residual deviance: 35.052 on 73 degrees of freedom AIC: 130.48 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- MADAGASCAR -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- MALAYSIA -- 61 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -15.194 -8.740 -1.330 5.955 36.244 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -11.10090 2.71519 -4.088 0.00011 *** x.var 0.47809 0.06208 7.701 5.13e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.64 on 73 degrees of freedom Multiple R-squared: 0.4482, Adjusted R-squared: 0.4407 F-statistic: 59.3 on 1 and 73 DF, p-value: 5.133e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.60159 -0.70060 -0.04311 0.77519 1.55150 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.077037 0.215427 -5.000 3.82e-06 *** x.var 0.048702 0.004926 9.887 4.12e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9235 on 73 degrees of freedom Multiple R-squared: 0.5725, Adjusted R-squared: 0.5666 F-statistic: 97.75 on 1 and 73 DF, p-value: 4.125e-15 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.66290 -0.84170 -0.18698 -0.03775 1.93542 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.546295 0.526560 -16.23 <2e-16 *** x.var 0.173064 0.007528 22.99 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1730.047 on 74 degrees of freedom Residual deviance: 74.137 on 73 degrees of freedom AIC: 171.07 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- MALDIVES -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- MALTA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- MAURITANIA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.29646 -0.17120 -0.04594 0.07933 0.69677 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.163964 0.059411 -2.760 0.00731 ** x.var 0.006771 0.001358 4.984 4.05e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2547 on 73 degrees of freedom Multiple R-squared: 0.2539, Adjusted R-squared: 0.2437 F-statistic: 24.84 on 1 and 73 DF, p-value: 4.052e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.20549 -0.11867 -0.03184 0.05499 0.48296 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1136512 0.0411802 -2.760 0.00731 ** x.var 0.0046933 0.0009416 4.984 4.05e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1765 on 73 degrees of freedom Multiple R-squared: 0.2539, Adjusted R-squared: 0.2437 F-statistic: 24.84 on 1 and 73 DF, p-value: 4.052e-06 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.68352 -0.06384 -0.00445 -0.00031 0.97769 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -21.0165 7.8649 -2.672 0.00754 ** x.var 0.2877 0.1091 2.637 0.00837 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 33.202 on 74 degrees of freedom Residual deviance: 4.248 on 73 degrees of freedom AIC: 22.248 Number of Fisher Scoring iterations: 9 -------------------------------------------------------------------------------- MAURITIUS -- 7 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.9477 -0.9831 -0.1988 0.5634 4.3418 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.276757 0.335510 -3.805 0.000292 *** x.var 0.054651 0.007672 7.124 6.13e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.438 on 73 degrees of freedom Multiple R-squared: 0.4101, Adjusted R-squared: 0.402 F-statistic: 50.75 on 1 and 73 DF, p-value: 6.135e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.73761 -0.36045 -0.00369 0.28059 1.07681 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.46521 0.10670 -4.360 4.19e-05 *** x.var 0.02039 0.00244 8.356 3.02e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4574 on 73 degrees of freedom Multiple R-squared: 0.4889, Adjusted R-squared: 0.4819 F-statistic: 69.82 on 1 and 73 DF, p-value: 3.015e-12 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.03691 -0.22959 -0.05073 -0.00936 1.15853 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.41035 1.66054 -6.871 6.35e-12 *** x.var 0.18288 0.02364 7.734 1.04e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 205.935 on 74 degrees of freedom Residual deviance: 11.875 on 73 degrees of freedom AIC: 64.523 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- MEXICO -- 79 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -11.441 -7.793 -2.061 4.019 61.306 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -8.36108 2.77032 -3.018 0.0035 ** x.var 0.34740 0.06334 5.484 5.68e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.88 on 73 degrees of freedom Multiple R-squared: 0.2918, Adjusted R-squared: 0.2821 F-statistic: 30.08 on 1 and 73 DF, p-value: 5.677e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.31745 -0.66198 -0.08189 0.51478 2.36958 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.883389 0.199360 -4.431 3.24e-05 *** x.var 0.038611 0.004558 8.470 1.84e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8546 on 73 degrees of freedom Multiple R-squared: 0.4957, Adjusted R-squared: 0.4888 F-statistic: 71.74 on 1 and 73 DF, p-value: 1.84e-12 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.23763 -0.17758 -0.02488 -0.00224 1.08762 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -15.11359 0.98356 -15.37 <2e-16 *** x.var 0.26047 0.01371 19.00 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1439.4545 on 74 degrees of freedom Residual deviance: 9.6687 on 73 degrees of freedom AIC: 87.767 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- MOLDOVA -- 15 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.0235 -1.2410 -0.5166 0.6450 11.7835 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.49261 0.50088 -2.980 0.00391 ** x.var 0.06279 0.01145 5.482 5.72e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.147 on 73 degrees of freedom Multiple R-squared: 0.2916, Adjusted R-squared: 0.2819 F-statistic: 30.06 on 1 and 73 DF, p-value: 5.721e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.68312 -0.30326 -0.05157 0.20498 1.69934 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.466726 0.107977 -4.322 4.80e-05 *** x.var 0.020533 0.002469 8.317 3.58e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4629 on 73 degrees of freedom Multiple R-squared: 0.4865, Adjusted R-squared: 0.4795 F-statistic: 69.16 on 1 and 73 DF, p-value: 3.579e-12 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.88891 -0.18380 -0.02912 -0.00380 1.12402 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -13.93145 1.91617 -7.270 3.58e-13 *** x.var 0.22020 0.02696 8.169 3.12e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 249.0411 on 74 degrees of freedom Residual deviance: 7.5407 on 73 degrees of freedom AIC: 63.231 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- MONACO -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.32778 -0.18672 -0.04567 0.09538 0.66460 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.183063 0.061677 -2.968 0.00405 ** x.var 0.007624 0.001410 5.406 7.75e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2644 on 73 degrees of freedom Multiple R-squared: 0.2859, Adjusted R-squared: 0.2761 F-statistic: 29.23 on 1 and 73 DF, p-value: 7.745e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.22720 -0.12943 -0.03166 0.06611 0.46067 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1268896 0.0427511 -2.968 0.00405 ** x.var 0.0052849 0.0009775 5.406 7.75e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1833 on 73 degrees of freedom Multiple R-squared: 0.2859, Adjusted R-squared: 0.2761 F-statistic: 29.23 on 1 and 73 DF, p-value: 7.745e-07 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.69004 -0.08695 -0.00963 -0.00094 0.98989 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -18.27322 6.37932 -2.864 0.00418 ** x.var 0.25131 0.08908 2.821 0.00479 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 35.8087 on 74 degrees of freedom Residual deviance: 4.8678 on 73 degrees of freedom AIC: 24.868 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- MONGOLIA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- MONTENEGRO -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.68152 -0.34783 -0.01415 0.24235 1.15615 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.418739 0.106552 -3.930 0.000191 *** x.var 0.018037 0.002436 7.403 1.85e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4568 on 73 degrees of freedom Multiple R-squared: 0.4288, Adjusted R-squared: 0.421 F-statistic: 54.81 on 1 and 73 DF, p-value: 1.85e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.41041 -0.21046 -0.01051 0.18944 0.59093 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.248884 0.061142 -4.071 0.000117 *** x.var 0.010808 0.001398 7.731 4.51e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2621 on 73 degrees of freedom Multiple R-squared: 0.4502, Adjusted R-squared: 0.4426 F-statistic: 59.77 on 1 and 73 DF, p-value: 4.507e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.75148 -0.18074 -0.04339 -0.00878 0.92452 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.8077 2.7069 -4.362 1.29e-05 *** x.var 0.1728 0.0387 4.466 7.98e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 69.5058 on 74 degrees of freedom Residual deviance: 7.0708 on 73 degrees of freedom AIC: 42.753 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- MOROCCO -- 70 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -12.338 -9.144 -1.871 5.473 48.596 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -9.76577 2.70456 -3.611 0.000557 *** x.var 0.41559 0.06184 6.720 3.42e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.59 on 73 degrees of freedom Multiple R-squared: 0.3822, Adjusted R-squared: 0.3737 F-statistic: 45.16 on 1 and 73 DF, p-value: 3.424e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.2228 -0.6215 -0.1576 0.5603 1.7893 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.000309 0.184621 -5.418 7.39e-07 *** x.var 0.046315 0.004221 10.971 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7915 on 73 degrees of freedom Multiple R-squared: 0.6225, Adjusted R-squared: 0.6173 F-statistic: 120.4 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.20623 -0.40641 -0.10988 -0.02022 2.21943 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.143410 0.643078 -15.77 <2e-16 *** x.var 0.193598 0.009121 21.23 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1539.993 on 74 degrees of freedom Residual deviance: 26.727 on 73 degrees of freedom AIC: 131.23 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- NAMIBIA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- NEPAL -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- NETHERLANDS -- 1771 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -358.96 -243.59 -55.74 144.22 1145.66 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -283.59 74.35 -3.814 0.000283 *** x.var 12.12 1.70 7.129 6.01e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 318.7 on 73 degrees of freedom Multiple R-squared: 0.4104, Adjusted R-squared: 0.4023 F-statistic: 50.82 on 1 and 73 DF, p-value: 6.012e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.4993 -1.2882 0.1276 1.2816 2.0189 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.123976 0.319409 -6.65 4.62e-09 *** x.var 0.105074 0.007303 14.39 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.369 on 73 degrees of freedom Multiple R-squared: 0.7393, Adjusted R-squared: 0.7357 F-statistic: 207 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -10.4866 -2.5622 -0.6451 -0.1271 6.1753 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -6.213970 0.113552 -54.72 <2e-16 *** x.var 0.185767 0.001615 115.02 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 43968.03 on 74 degrees of freedom Residual deviance: 633.45 on 73 degrees of freedom AIC: 832.28 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- NEW ZEALAND -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.32778 -0.18672 -0.04567 0.09538 0.66460 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.183063 0.061677 -2.968 0.00405 ** x.var 0.007624 0.001410 5.406 7.75e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2644 on 73 degrees of freedom Multiple R-squared: 0.2859, Adjusted R-squared: 0.2761 F-statistic: 29.23 on 1 and 73 DF, p-value: 7.745e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.22720 -0.12943 -0.03166 0.06611 0.46067 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1268896 0.0427511 -2.968 0.00405 ** x.var 0.0052849 0.0009775 5.406 7.75e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1833 on 73 degrees of freedom Multiple R-squared: 0.2859, Adjusted R-squared: 0.2761 F-statistic: 29.23 on 1 and 73 DF, p-value: 7.745e-07 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.69004 -0.08695 -0.00963 -0.00094 0.98989 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -18.27322 6.37932 -2.864 0.00418 ** x.var 0.25131 0.08908 2.821 0.00479 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 35.8087 on 74 degrees of freedom Residual deviance: 4.8678 on 73 degrees of freedom AIC: 24.868 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- NICARAGUA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.38298 -0.21193 -0.04087 0.13018 0.60778 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.218018 0.064963 -3.356 0.00126 ** x.var 0.009246 0.001485 6.225 2.75e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2785 on 73 degrees of freedom Multiple R-squared: 0.3467, Adjusted R-squared: 0.3378 F-statistic: 38.75 on 1 and 73 DF, p-value: 2.75e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.26546 -0.14690 -0.02833 0.09023 0.42128 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.151119 0.045029 -3.356 0.00126 ** x.var 0.006409 0.001030 6.225 2.75e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.193 on 73 degrees of freedom Multiple R-squared: 0.3467, Adjusted R-squared: 0.3378 F-statistic: 38.75 on 1 and 73 DF, p-value: 2.75e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.69917 -0.14782 -0.02307 -0.00361 1.00691 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -14.45240 4.49254 -3.217 0.00130 ** x.var 0.20067 0.06357 3.157 0.00159 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 40.2981 on 74 degrees of freedom Residual deviance: 6.1036 on 73 degrees of freedom AIC: 30.104 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- NIGER -- 10 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.6514 -0.8746 -0.2885 0.4994 7.8375 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.031712 0.358512 -2.878 0.00525 ** x.var 0.042589 0.008198 5.195 1.78e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.537 on 73 degrees of freedom Multiple R-squared: 0.2699, Adjusted R-squared: 0.2599 F-statistic: 26.99 on 1 and 73 DF, p-value: 1.784e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.59660 -0.31504 -0.03348 0.17198 1.61866 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.362218 0.105069 -3.447 0.000943 *** x.var 0.015219 0.002402 6.335 1.74e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4504 on 73 degrees of freedom Multiple R-squared: 0.3547, Adjusted R-squared: 0.3459 F-statistic: 40.13 on 1 and 73 DF, p-value: 1.736e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.82765 -0.09632 -0.00805 -0.00055 0.76780 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -19.31556 3.15890 -6.115 9.68e-10 *** x.var 0.28959 0.04381 6.610 3.84e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 187.7195 on 74 degrees of freedom Residual deviance: 5.1453 on 73 degrees of freedom AIC: 43.666 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- NIGERIA -- 5 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.9740 -0.4909 -0.0861 0.2926 3.6604 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.619099 0.184307 -3.359 0.00125 ** x.var 0.026117 0.004214 6.197 3.08e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7901 on 73 degrees of freedom Multiple R-squared: 0.3447, Adjusted R-squared: 0.3358 F-statistic: 38.41 on 1 and 73 DF, p-value: 3.082e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.48742 -0.24827 -0.00911 0.16941 1.12336 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.301137 0.077211 -3.900 0.000212 *** x.var 0.012927 0.001765 7.322 2.62e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.331 on 73 degrees of freedom Multiple R-squared: 0.4234, Adjusted R-squared: 0.4156 F-statistic: 53.61 on 1 and 73 DF, p-value: 2.621e-10 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.73825 -0.16346 -0.02447 -0.00342 0.92124 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -14.28832 2.85995 -4.996 5.85e-07 *** x.var 0.21292 0.04031 5.281 1.28e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 104.5413 on 74 degrees of freedom Residual deviance: 5.4901 on 73 degrees of freedom AIC: 43.957 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- NORTH MACEDONIA -- 18 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -3.7065 -1.9896 -0.5316 1.2607 12.7572 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.4386 0.7171 -3.401 0.00109 ** x.var 0.1024 0.0164 6.246 2.51e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.074 on 73 degrees of freedom Multiple R-squared: 0.3483, Adjusted R-squared: 0.3394 F-statistic: 39.01 on 1 and 73 DF, p-value: 2.514e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.9379 -0.4672 -0.0218 0.3405 1.6248 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.588966 0.142206 -4.142 9.16e-05 *** x.var 0.025449 0.003252 7.826 2.98e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6096 on 73 degrees of freedom Multiple R-squared: 0.4563, Adjusted R-squared: 0.4488 F-statistic: 61.25 on 1 and 73 DF, p-value: 2.982e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.22916 -0.19312 -0.03025 -0.00380 0.77134 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -13.75218 1.53373 -8.967 <2e-16 *** x.var 0.22453 0.02155 10.418 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 408.731 on 74 degrees of freedom Residual deviance: 11.614 on 73 degrees of freedom AIC: 68.858 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- NORWAY -- 71 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -13.490 -9.041 -2.250 5.009 46.739 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -10.86234 2.79121 -3.892 0.000218 *** x.var 0.46831 0.06382 7.338 2.45e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 11.97 on 73 degrees of freedom Multiple R-squared: 0.4245, Adjusted R-squared: 0.4166 F-statistic: 53.84 on 1 and 73 DF, p-value: 2.452e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.55081 -0.61254 0.07217 0.60468 1.55935 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.086491 0.193106 -5.626 3.2e-07 *** x.var 0.050717 0.004415 11.486 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8278 on 73 degrees of freedom Multiple R-squared: 0.6438, Adjusted R-squared: 0.6389 F-statistic: 131.9 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.78103 -0.43303 -0.10509 -0.02345 1.12028 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.457934 0.526518 -16.06 <2e-16 *** x.var 0.171523 0.007533 22.77 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1643.577 on 74 degrees of freedom Residual deviance: 28.188 on 73 degrees of freedom AIC: 135.53 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- OMAN -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.35361 -0.20624 -0.05887 0.08850 1.60656 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.196036 0.081524 -2.405 0.0187 * x.var 0.007966 0.001864 4.273 5.73e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3495 on 73 degrees of freedom Multiple R-squared: 0.2001, Adjusted R-squared: 0.1891 F-statistic: 18.26 on 1 and 73 DF, p-value: 5.731e-05 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.21891 -0.12781 -0.03672 0.05438 0.85508 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.120850 0.048488 -2.492 0.015 * x.var 0.004924 0.001109 4.441 3.12e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2079 on 73 degrees of freedom Multiple R-squared: 0.2127, Adjusted R-squared: 0.2019 F-statistic: 19.72 on 1 and 73 DF, p-value: 3.12e-05 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.68427 -0.02425 -0.00057 -0.00001 0.89181 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -29.4291 10.5424 -2.791 0.00525 ** x.var 0.4055 0.1443 2.809 0.00497 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 41.3539 on 74 degrees of freedom Residual deviance: 2.8268 on 73 degrees of freedom AIC: 20.054 Number of Fisher Scoring iterations: 9 -------------------------------------------------------------------------------- PAKISTAN -- 47 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -9.767 -5.529 -1.398 3.241 31.971 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -6.89477 1.91017 -3.610 0.000559 *** x.var 0.29232 0.04368 6.693 3.85e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 8.189 on 73 degrees of freedom Multiple R-squared: 0.3803, Adjusted R-squared: 0.3718 F-statistic: 44.79 on 1 and 73 DF, p-value: 3.848e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.38051 -0.64057 -0.02062 0.55933 1.77075 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.899299 0.189908 -4.735 1.05e-05 *** x.var 0.039997 0.004342 9.211 7.50e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8141 on 73 degrees of freedom Multiple R-squared: 0.5375, Adjusted R-squared: 0.5312 F-statistic: 84.84 on 1 and 73 DF, p-value: 7.501e-14 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.75367 -0.33359 -0.05175 -0.00890 1.03831 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.04447 0.80192 -13.77 <2e-16 *** x.var 0.20131 0.01134 17.75 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1107.520 on 74 degrees of freedom Residual deviance: 24.965 on 73 degrees of freedom AIC: 106.86 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- PANAMA -- 46 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -9.801 -5.730 -1.631 3.545 30.762 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -6.94667 1.91002 -3.637 0.000511 *** x.var 0.29579 0.04367 6.773 2.74e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 8.188 on 73 degrees of freedom Multiple R-squared: 0.3859, Adjusted R-squared: 0.3775 F-statistic: 45.87 on 1 and 73 DF, p-value: 2.741e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.12029 -0.62941 -0.08954 0.48453 1.65789 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.899971 0.173810 -5.178 1.91e-06 *** x.var 0.041230 0.003974 10.374 5.20e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7451 on 73 degrees of freedom Multiple R-squared: 0.5958, Adjusted R-squared: 0.5903 F-statistic: 107.6 on 1 and 73 DF, p-value: 5.204e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.42849 -0.32254 -0.07841 -0.01323 1.72072 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.40431 0.75724 -13.74 <2e-16 *** x.var 0.19249 0.01074 17.92 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1094.013 on 74 degrees of freedom Residual deviance: 19.679 on 73 degrees of freedom AIC: 114.59 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- PAPUA NEW GUINEA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- PARAGUAY -- 3 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.29526 -0.63578 -0.08325 0.56028 1.52651 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.807928 0.188998 -4.275 5.7e-05 *** x.var 0.035647 0.004322 8.249 4.8e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8102 on 73 degrees of freedom Multiple R-squared: 0.4824, Adjusted R-squared: 0.4753 F-statistic: 68.04 on 1 and 73 DF, p-value: 4.799e-12 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.62223 -0.30681 0.00862 0.27290 0.67881 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.383723 0.087441 -4.388 3.78e-05 *** x.var 0.017050 0.001999 8.528 1.43e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3748 on 73 degrees of freedom Multiple R-squared: 0.499, Adjusted R-squared: 0.4922 F-statistic: 72.72 on 1 and 73 DF, p-value: 1.434e-12 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.17163 -0.34209 -0.09669 -0.02737 1.45959 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.11821 1.55640 -5.859 4.67e-09 *** x.var 0.14433 0.02259 6.390 1.66e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 131.394 on 74 degrees of freedom Residual deviance: 18.134 on 73 degrees of freedom AIC: 66.65 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- PERU -- 83 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -14.581 -8.807 -2.430 4.690 61.148 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -10.22631 3.19863 -3.197 0.00205 ** x.var 0.42771 0.07314 5.848 1.3e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 13.71 on 73 degrees of freedom Multiple R-squared: 0.319, Adjusted R-squared: 0.3097 F-statistic: 34.2 on 1 and 73 DF, p-value: 1.302e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.50429 -0.71608 0.02953 0.56303 2.20222 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.96687 0.21256 -4.549 2.10e-05 *** x.var 0.04261 0.00486 8.766 5.11e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9112 on 73 degrees of freedom Multiple R-squared: 0.5128, Adjusted R-squared: 0.5062 F-statistic: 76.85 on 1 and 73 DF, p-value: 5.114e-13 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.84211 -0.25337 -0.03576 -0.00413 1.23233 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -13.21798 0.79767 -16.57 <2e-16 *** x.var 0.23701 0.01117 21.21 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1718.331 on 74 degrees of freedom Residual deviance: 22.875 on 73 degrees of freedom AIC: 105.77 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- PHILIPPINES -- 152 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -30.444 -20.185 -4.335 13.302 92.447 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -25.1622 6.0554 -4.155 8.72e-05 *** x.var 1.1295 0.1385 8.158 7.11e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 25.96 on 73 degrees of freedom Multiple R-squared: 0.4769, Adjusted R-squared: 0.4697 F-statistic: 66.55 on 1 and 73 DF, p-value: 7.113e-12 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.5886 -0.5592 0.1514 0.5603 1.2033 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.837618 0.176558 -4.744 1.01e-05 *** x.var 0.062387 0.004037 15.453 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7569 on 73 degrees of freedom Multiple R-squared: 0.7659, Adjusted R-squared: 0.7627 F-statistic: 238.8 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.62567 -0.36140 -0.01687 0.85039 2.07382 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -4.666745 0.244095 -19.12 <2e-16 *** x.var 0.130099 0.003577 36.37 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 3492.550 on 74 degrees of freedom Residual deviance: 88.098 on 73 degrees of freedom AIC: 302.83 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- POLAND -- 94 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -13.638 -10.325 -3.350 5.568 67.719 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -11.9377 3.4421 -3.468 0.000883 *** x.var 0.5096 0.0787 6.475 9.66e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 14.76 on 73 degrees of freedom Multiple R-squared: 0.3648, Adjusted R-squared: 0.3561 F-statistic: 41.92 on 1 and 73 DF, p-value: 9.657e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.47173 -0.57595 -0.03828 0.58472 1.80246 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.087652 0.187578 -5.798 1.59e-07 *** x.var 0.051188 0.004289 11.934 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8041 on 73 degrees of freedom Multiple R-squared: 0.6611, Adjusted R-squared: 0.6565 F-statistic: 142.4 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.32118 -0.33908 -0.08684 -0.01682 1.25505 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.523239 0.560567 -16.99 <2e-16 *** x.var 0.187743 0.007967 23.56 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1849.687 on 74 degrees of freedom Residual deviance: 18.994 on 73 degrees of freedom AIC: 130.37 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- PORTUGAL -- 295 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -60.80 -38.69 -8.00 22.69 199.58 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -44.0800 12.3576 -3.567 0.000642 *** x.var 1.8600 0.2826 6.583 6.13e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 52.98 on 73 degrees of freedom Multiple R-squared: 0.3725, Adjusted R-squared: 0.3639 F-statistic: 43.33 on 1 and 73 DF, p-value: 6.129e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.11248 -1.04752 0.01744 0.95332 2.28701 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.437393 0.292389 -4.916 5.27e-06 *** x.var 0.064543 0.006686 9.654 1.12e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.253 on 73 degrees of freedom Multiple R-squared: 0.5608, Adjusted R-squared: 0.5548 F-statistic: 93.2 on 1 and 73 DF, p-value: 1.117e-14 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -4.6169 -1.0141 -0.1883 -0.0265 3.3883 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.976250 0.337722 -29.54 <2e-16 *** x.var 0.212273 0.004762 44.58 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 7211.69 on 74 degrees of freedom Residual deviance: 166.41 on 73 degrees of freedom AIC: 281.82 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- QATAR -- 4 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.81073 -0.45126 -0.09179 0.20939 3.01439 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.471712 0.162431 -2.904 0.00487 ** x.var 0.019431 0.003714 5.232 1.55e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6963 on 73 degrees of freedom Multiple R-squared: 0.2727, Adjusted R-squared: 0.2627 F-statistic: 27.37 on 1 and 73 DF, p-value: 1.546e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.40390 -0.22553 -0.04716 0.13121 1.11876 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.232449 0.075257 -3.089 0.00284 ** x.var 0.009642 0.001721 5.603 3.52e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3226 on 73 degrees of freedom Multiple R-squared: 0.3007, Adjusted R-squared: 0.2911 F-statistic: 31.39 on 1 and 73 DF, p-value: 3.522e-07 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.83278 -0.06985 -0.00431 -0.00027 0.71172 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -20.9089 4.8755 -4.289 1.80e-05 *** x.var 0.3008 0.0675 4.455 8.38e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 89.2808 on 74 degrees of freedom Residual deviance: 4.7902 on 73 degrees of freedom AIC: 32.259 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- ROMANIA -- 151 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -32.551 -19.084 -4.551 10.353 104.900 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -21.6432 6.6091 -3.275 0.00162 ** x.var 0.9032 0.1511 5.977 7.67e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 28.33 on 73 degrees of freedom Multiple R-squared: 0.3286, Adjusted R-squared: 0.3194 F-statistic: 35.72 on 1 and 73 DF, p-value: 7.668e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.8269 -0.9134 -0.0493 0.7654 2.4723 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.135759 0.268628 -4.228 6.74e-05 *** x.var 0.049377 0.006142 8.039 1.19e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.152 on 73 degrees of freedom Multiple R-squared: 0.4696, Adjusted R-squared: 0.4623 F-statistic: 64.62 on 1 and 73 DF, p-value: 1.19e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -4.0289 -0.5447 -0.0578 -0.0061 2.5841 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -12.987688 0.567638 -22.88 <2e-16 *** x.var 0.244211 0.007939 30.76 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 3716.093 on 74 degrees of freedom Residual deviance: 92.239 on 73 degrees of freedom AIC: 178.72 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- RUSSIA -- 45 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -7.614 -5.283 -1.513 2.370 33.723 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -5.36577 1.84361 -2.910 0.00478 ** x.var 0.22191 0.04215 5.264 1.36e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 7.903 on 73 degrees of freedom Multiple R-squared: 0.2751, Adjusted R-squared: 0.2652 F-statistic: 27.71 on 1 and 73 DF, p-value: 1.361e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.07359 -0.53688 -0.09122 0.36828 2.18465 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.732709 0.177343 -4.132 9.49e-05 *** x.var 0.031689 0.004055 7.815 3.14e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7603 on 73 degrees of freedom Multiple R-squared: 0.4555, Adjusted R-squared: 0.4481 F-statistic: 61.07 on 1 and 73 DF, p-value: 3.136e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.55812 -0.16950 -0.01451 -0.00109 1.26684 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -17.01584 1.33662 -12.73 <2e-16 *** x.var 0.28061 0.01856 15.12 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 951.516 on 74 degrees of freedom Residual deviance: 11.533 on 73 degrees of freedom AIC: 79.609 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- RWANDA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- SAINT LUCIA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- SAINT VINCENT AND THE GRENADINES -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- SAN MARINO -- 32 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -8.9675 -5.7220 -0.0677 4.9464 12.3412 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.92432 1.38891 -5.705 2.33e-07 *** x.var 0.37275 0.03176 11.737 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.954 on 73 degrees of freedom Multiple R-squared: 0.6536, Adjusted R-squared: 0.6489 F-statistic: 137.8 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.19177 -0.56823 0.07064 0.53169 0.98699 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.041457 0.144793 -7.193 4.57e-10 *** x.var 0.054469 0.003311 16.452 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6207 on 73 degrees of freedom Multiple R-squared: 0.7876, Adjusted R-squared: 0.7847 F-statistic: 270.7 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.2771 -0.8640 -0.4598 -0.2039 2.9032 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -4.031811 0.327588 -12.31 <2e-16 *** x.var 0.104997 0.004914 21.37 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1075.032 on 74 degrees of freedom Residual deviance: 76.709 on 73 degrees of freedom AIC: 214.31 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- SAUDI ARABIA -- 34 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -6.0000 -3.5467 -0.9155 1.7061 25.9344 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.8512 1.3207 -2.916 0.00471 ** x.var 0.1589 0.0302 5.262 1.37e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.662 on 73 degrees of freedom Multiple R-squared: 0.275, Adjusted R-squared: 0.265 F-statistic: 27.68 on 1 and 73 DF, p-value: 1.375e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.03412 -0.53612 -0.06504 0.35220 2.17129 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.634843 0.170867 -3.715 0.000394 *** x.var 0.026919 0.003907 6.890 1.67e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7325 on 73 degrees of freedom Multiple R-squared: 0.394, Adjusted R-squared: 0.3857 F-statistic: 47.47 on 1 and 73 DF, p-value: 1.665e-09 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.36359 -0.14188 -0.00947 -0.00074 1.05664 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -18.19734 1.65251 -11.01 <2e-16 *** x.var 0.29233 0.02291 12.76 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 695.65 on 74 degrees of freedom Residual deviance: 12.08 on 73 degrees of freedom AIC: 67.131 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- SENEGAL -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.31911 -0.18858 -0.05806 0.07247 1.65267 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.174775 0.080051 -2.183 0.032230 * x.var 0.007055 0.001830 3.855 0.000247 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3432 on 73 degrees of freedom Multiple R-squared: 0.1691, Adjusted R-squared: 0.1577 F-statistic: 14.86 on 1 and 73 DF, p-value: 0.0002472 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.19440 -0.11498 -0.03556 0.04386 0.88704 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.106113 0.047001 -2.258 0.026956 * x.var 0.004293 0.001075 3.995 0.000153 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2015 on 73 degrees of freedom Multiple R-squared: 0.1794, Adjusted R-squared: 0.1681 F-statistic: 15.96 on 1 and 73 DF, p-value: 0.000153 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.68120 -0.01180 -0.00012 0.00000 0.83442 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -35.9343 13.8116 -2.602 0.00928 ** x.var 0.4925 0.1880 2.619 0.00881 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 38.7473 on 74 degrees of freedom Residual deviance: 2.3594 on 73 degrees of freedom AIC: 17.587 Number of Fisher Scoring iterations: 10 -------------------------------------------------------------------------------- SERBIA -- 51 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -9.607 -5.925 -1.242 2.934 38.115 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -6.09838 2.01458 -3.027 0.00341 ** x.var 0.25312 0.04606 5.495 5.44e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 8.636 on 73 degrees of freedom Multiple R-squared: 0.2926, Adjusted R-squared: 0.2829 F-statistic: 30.19 on 1 and 73 DF, p-value: 5.441e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.16304 -0.61313 -0.09655 0.42003 2.22163 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.769975 0.188882 -4.076 0.000115 *** x.var 0.033328 0.004319 7.717 4.79e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8097 on 73 degrees of freedom Multiple R-squared: 0.4493, Adjusted R-squared: 0.4417 F-statistic: 59.55 on 1 and 73 DF, p-value: 4.789e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.42197 -0.17166 -0.01928 -0.00167 1.38502 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -15.73844 1.17329 -13.41 <2e-16 *** x.var 0.26475 0.01634 16.20 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 1072.088 on 74 degrees of freedom Residual deviance: 23.698 on 73 degrees of freedom AIC: 92.733 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- SEYCHELLES -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- SINGAPORE -- 6 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.5704 -0.7625 0.0454 0.5039 3.7745 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.006126 0.247160 -4.071 0.000117 *** x.var 0.043670 0.005651 7.727 4.58e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.06 on 73 degrees of freedom Multiple R-squared: 0.4499, Adjusted R-squared: 0.4424 F-statistic: 59.71 on 1 and 73 DF, p-value: 4.578e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.68164 -0.33518 0.01128 0.30776 0.98336 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.423286 0.095176 -4.447 3.05e-05 *** x.var 0.018728 0.002176 8.605 1.02e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.408 on 73 degrees of freedom Multiple R-squared: 0.5036, Adjusted R-squared: 0.4968 F-statistic: 74.05 on 1 and 73 DF, p-value: 1.025e-12 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.04577 -0.27427 -0.08447 -0.01883 1.35063 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.18104 1.61633 -6.299 3.00e-10 *** x.var 0.16233 0.02322 6.990 2.75e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 160.450 on 74 degrees of freedom Residual deviance: 13.614 on 73 degrees of freedom AIC: 64.552 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- SLOVAKIA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.35397 -0.18946 -0.05420 0.08107 0.74108 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.157838 0.067104 -2.352 0.0214 * x.var 0.007312 0.001534 4.765 9.35e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2877 on 73 degrees of freedom Multiple R-squared: 0.2373, Adjusted R-squared: 0.2268 F-statistic: 22.71 on 1 and 73 DF, p-value: 9.355e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.24535 -0.13132 -0.03757 0.05619 0.51368 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.109405 0.046513 -2.352 0.0214 * x.var 0.005068 0.001064 4.765 9.35e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1994 on 73 degrees of freedom Multiple R-squared: 0.2373, Adjusted R-squared: 0.2268 F-statistic: 22.71 on 1 and 73 DF, p-value: 9.355e-06 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.04333 -0.29705 -0.10567 -0.03762 1.54132 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.42745 2.52183 -3.342 0.000832 *** x.var 0.11170 0.03756 2.974 0.002938 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 38.165 on 74 degrees of freedom Residual deviance: 17.875 on 73 degrees of freedom AIC: 39.875 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- SLOVENIA -- 28 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -5.1738 -3.3935 -0.7816 1.9988 19.2986 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.93658 1.04128 -3.781 0.000317 *** x.var 0.16851 0.02381 7.077 7.49e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 4.464 on 73 degrees of freedom Multiple R-squared: 0.4069, Adjusted R-squared: 0.3988 F-statistic: 50.09 on 1 and 73 DF, p-value: 7.487e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.02607 -0.50797 -0.06553 0.46146 1.55220 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.757799 0.149470 -5.07 2.91e-06 *** x.var 0.034305 0.003418 10.04 2.17e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6408 on 73 degrees of freedom Multiple R-squared: 0.5799, Adjusted R-squared: 0.5741 F-statistic: 100.8 on 1 and 73 DF, p-value: 2.172e-15 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.96013 -0.30425 -0.07422 -0.01369 1.15582 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.28328 0.94559 -10.88 <2e-16 *** x.var 0.18286 0.01346 13.58 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 609.847 on 74 degrees of freedom Residual deviance: 11.527 on 73 degrees of freedom AIC: 92.895 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- SOMALIA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- SOUTH AFRICA -- 11 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.0250 -1.1265 -0.2815 0.5903 8.4768 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.212973 0.432627 -2.804 0.00647 ** x.var 0.049815 0.009892 5.036 3.32e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.855 on 73 degrees of freedom Multiple R-squared: 0.2578, Adjusted R-squared: 0.2477 F-statistic: 25.36 on 1 and 73 DF, p-value: 3.322e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.63895 -0.35040 -0.06185 0.19551 1.68998 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.374884 0.117080 -3.202 0.00202 ** x.var 0.015598 0.002677 5.826 1.42e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5019 on 73 degrees of freedom Multiple R-squared: 0.3174, Adjusted R-squared: 0.3081 F-statistic: 33.95 on 1 and 73 DF, p-value: 1.423e-07 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.07313 -0.09231 -0.00561 -0.00030 1.16259 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -21.25149 3.24219 -6.555 5.58e-11 *** x.var 0.31845 0.04478 7.111 1.15e-12 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 228.8388 on 74 degrees of freedom Residual deviance: 7.6012 on 73 degrees of freedom AIC: 44.007 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- SPAIN -- 12641 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2804.1 -1910.7 -382.2 1246.5 7520.7 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2282.98 554.15 -4.12 9.89e-05 *** x.var 98.71 12.67 7.79 3.49e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2376 on 73 degrees of freedom Multiple R-squared: 0.454, Adjusted R-squared: 0.4465 F-statistic: 60.69 on 1 and 73 DF, p-value: 3.487e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -3.2566 -1.2927 0.4484 1.3853 2.6715 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.819676 0.381566 -7.39 1.96e-10 *** x.var 0.148203 0.008725 16.99 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.636 on 73 degrees of freedom Multiple R-squared: 0.7981, Adjusted R-squared: 0.7953 F-statistic: 288.5 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -34.845 -8.872 -2.803 -0.596 21.856 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.8177796 0.0352556 -79.92 <2e-16 *** x.var 0.1674291 0.0005053 331.33 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 344715.0 on 74 degrees of freedom Residual deviance: 7990.9 on 73 degrees of freedom AIC: 8266.9 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- SRI LANKA -- 5 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.0968 -0.6100 -0.1495 0.3111 3.6926 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.640000 0.223373 -2.865 0.00544 ** x.var 0.026316 0.005108 5.152 2.11e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9576 on 73 degrees of freedom Multiple R-squared: 0.2667, Adjusted R-squared: 0.2566 F-statistic: 26.55 on 1 and 73 DF, p-value: 2.111e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.47377 -0.26445 -0.05513 0.15419 1.22747 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.272989 0.088681 -3.078 0.00293 ** x.var 0.011315 0.002028 5.580 3.87e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3802 on 73 degrees of freedom Multiple R-squared: 0.299, Adjusted R-squared: 0.2894 F-statistic: 31.14 on 1 and 73 DF, p-value: 3.866e-07 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.93716 -0.07222 -0.00405 -0.00023 0.75150 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -21.3526 4.3473 -4.912 9.03e-07 *** x.var 0.3111 0.0601 5.175 2.28e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 121.6755 on 74 degrees of freedom Residual deviance: 5.8086 on 73 degrees of freedom AIC: 35.521 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- SUDAN -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.7277 -0.2803 0.0063 0.2318 0.8370 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.505586 0.093115 -5.43 7.06e-07 *** x.var 0.024182 0.002129 11.36 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3992 on 73 degrees of freedom Multiple R-squared: 0.6386, Adjusted R-squared: 0.6337 F-statistic: 129 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.45223 -0.17817 0.03663 0.17581 0.37973 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.303276 0.053545 -5.664 2.75e-07 *** x.var 0.014814 0.001224 12.100 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2295 on 73 degrees of freedom Multiple R-squared: 0.6673, Adjusted R-squared: 0.6627 F-statistic: 146.4 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.73799 -0.34318 -0.15949 -0.07059 1.00255 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -6.34146 1.19514 -5.306 1.12e-07 *** x.var 0.09884 0.01806 5.472 4.44e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 74.185 on 74 degrees of freedom Residual deviance: 11.697 on 73 degrees of freedom AIC: 67.993 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- SURINAME -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.14447 -0.08762 -0.03078 0.02606 0.85246 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0767568 0.0435822 -1.761 0.08239 . x.var 0.0030725 0.0009965 3.083 0.00289 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1868 on 73 degrees of freedom Multiple R-squared: 0.1152, Adjusted R-squared: 0.1031 F-statistic: 9.506 on 1 and 73 DF, p-value: 0.002889 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.10014 -0.06074 -0.02134 0.01806 0.59088 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0532037 0.0302089 -1.761 0.08239 . x.var 0.0021297 0.0006907 3.083 0.00289 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1295 on 73 degrees of freedom Multiple R-squared: 0.1152, Adjusted R-squared: 0.1031 F-statistic: 9.506 on 1 and 73 DF, p-value: 0.002889 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.61237 -0.00144 0.00000 0.00000 0.84360 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -51.5806 30.2159 -1.707 0.0878 . x.var 0.6931 0.4082 1.698 0.0895 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 19.3133 on 74 degrees of freedom Residual deviance: 1.7261 on 73 degrees of freedom AIC: 11.726 Number of Fisher Scoring iterations: 11 -------------------------------------------------------------------------------- SWEDEN -- 401 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -72.88 -50.88 -10.54 27.35 275.56 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -57.9106 16.8120 -3.445 0.000951 *** x.var 2.4447 0.3844 6.359 1.57e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 72.07 on 73 degrees of freedom Multiple R-squared: 0.3565, Adjusted R-squared: 0.3477 F-statistic: 40.44 on 1 and 73 DF, p-value: 1.566e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.01764 -0.99575 0.02788 1.00363 2.11237 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.562021 0.272063 -5.741 2.01e-07 *** x.var 0.073054 0.006221 11.743 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.166 on 73 degrees of freedom Multiple R-squared: 0.6539, Adjusted R-squared: 0.6491 F-statistic: 137.9 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -4.5694 -0.7806 -0.2253 -0.0349 3.3067 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -9.626677 0.292899 -32.87 <2e-16 *** x.var 0.211206 0.004131 51.13 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 9332.590 on 74 degrees of freedom Residual deviance: 92.366 on 73 degrees of freedom AIC: 232.41 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- SWITZERLAND -- 715 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -145.52 -92.95 -18.93 58.90 455.05 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -116.6476 29.5502 -3.947 0.00018 *** x.var 5.0213 0.6757 7.431 1.64e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 126.7 on 73 degrees of freedom Multiple R-squared: 0.4307, Adjusted R-squared: 0.4229 F-statistic: 55.23 on 1 and 73 DF, p-value: 1.639e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.10351 -1.11513 0.03485 1.06476 1.75351 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.845347 0.273096 -6.757 2.93e-09 *** x.var 0.091834 0.006244 14.706 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.171 on 73 degrees of freedom Multiple R-squared: 0.7476, Adjusted R-squared: 0.7442 F-statistic: 216.3 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -5.9929 -1.6202 -0.5007 -0.1095 3.2039 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -6.248261 0.163313 -38.26 <2e-16 *** x.var 0.173821 0.002334 74.47 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 17654.12 on 74 degrees of freedom Residual deviance: 228.01 on 73 degrees of freedom AIC: 410.91 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- TAIWAN* -- 5 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.2157 -0.5121 -0.1733 0.3169 2.2110 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.807207 0.191628 -4.212 7.13e-05 *** x.var 0.052119 0.004382 11.895 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8215 on 73 degrees of freedom Multiple R-squared: 0.6597, Adjusted R-squared: 0.655 F-statistic: 141.5 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.37965 -0.16995 -0.02837 0.15927 0.46921 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.244092 0.051570 -4.733 1.06e-05 *** x.var 0.022705 0.001179 19.255 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2211 on 73 degrees of freedom Multiple R-squared: 0.8355, Adjusted R-squared: 0.8332 F-statistic: 370.7 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.7688 -0.5401 -0.3081 0.3332 0.9782 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.627531 0.426014 -6.168 6.93e-10 *** x.var 0.056165 0.007012 8.009 1.15e-15 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 112.253 on 74 degrees of freedom Residual deviance: 21.412 on 73 degrees of freedom AIC: 141.91 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- TANZANIA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.26256 -0.15361 -0.04467 0.06428 0.73155 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.143784 0.056621 -2.539 0.0132 * x.var 0.005889 0.001295 4.549 2.1e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2427 on 73 degrees of freedom Multiple R-squared: 0.2208, Adjusted R-squared: 0.2102 F-statistic: 20.69 on 1 and 73 DF, p-value: 2.102e-05 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.18199 -0.10648 -0.03096 0.04456 0.50707 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0996633 0.0392468 -2.539 0.0132 * x.var 0.0040820 0.0008974 4.549 2.1e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1682 on 73 degrees of freedom Multiple R-squared: 0.2208, Adjusted R-squared: 0.2102 F-statistic: 20.69 on 1 and 73 DF, p-value: 2.102e-05 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.67480 -0.04218 -0.00187 -0.00008 0.96136 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -24.6964 10.0142 -2.466 0.0137 * x.var 0.3365 0.1380 2.438 0.0148 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 30.3087 on 74 degrees of freedom Residual deviance: 3.6262 on 73 degrees of freedom AIC: 19.626 Number of Fisher Scoring iterations: 9 -------------------------------------------------------------------------------- THAILAND -- 23 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -4.4759 -2.1322 -0.8982 1.3907 15.7207 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.12468 0.86293 -3.621 0.000538 *** x.var 0.13872 0.01973 7.030 9.15e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.699 on 73 degrees of freedom Multiple R-squared: 0.4037, Adjusted R-squared: 0.3956 F-statistic: 49.43 on 1 and 73 DF, p-value: 9.148e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.71071 -0.38027 -0.08285 0.29694 1.34487 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.643703 0.114032 -5.645 2.97e-07 *** x.var 0.033025 0.002607 12.666 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4888 on 73 degrees of freedom Multiple R-squared: 0.6873, Adjusted R-squared: 0.683 F-statistic: 160.4 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.54943 -0.31526 -0.11540 0.01115 1.40126 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.37139 0.75289 -9.791 <2e-16 *** x.var 0.13879 0.01097 12.658 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 454.560 on 74 degrees of freedom Residual deviance: 22.353 on 73 degrees of freedom AIC: 124.81 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- TOGO -- 3 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.70703 -0.38703 -0.06703 0.22541 2.15459 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.417297 0.136955 -3.047 0.00322 ** x.var 0.017297 0.003132 5.524 4.85e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5871 on 73 degrees of freedom Multiple R-squared: 0.2948, Adjusted R-squared: 0.2851 F-statistic: 30.51 on 1 and 73 DF, p-value: 4.849e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.38237 -0.21011 -0.03784 0.13442 0.92943 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.222879 0.068243 -3.266 0.00166 ** x.var 0.009311 0.001560 5.967 7.98e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2925 on 73 degrees of freedom Multiple R-squared: 0.3279, Adjusted R-squared: 0.3187 F-statistic: 35.61 on 1 and 73 DF, p-value: 7.978e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.75588 -0.09326 -0.00764 -0.00063 0.84491 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -18.8218 4.5928 -4.098 4.17e-05 *** x.var 0.2703 0.0639 4.230 2.34e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 76.6964 on 74 degrees of freedom Residual deviance: 4.4735 on 73 degrees of freedom AIC: 32.676 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- TRINIDAD AND TOBAGO -- 7 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.6633 -0.8765 -0.1747 0.5696 4.8263 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.016216 0.302073 -3.364 0.00123 ** x.var 0.042532 0.006907 6.158 3.63e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.295 on 73 degrees of freedom Multiple R-squared: 0.3419, Adjusted R-squared: 0.3328 F-statistic: 37.92 on 1 and 73 DF, p-value: 3.631e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.64113 -0.34020 -0.03927 0.22913 1.24311 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.383668 0.104853 -3.659 0.000475 *** x.var 0.016267 0.002398 6.785 2.6e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4495 on 73 degrees of freedom Multiple R-squared: 0.3867, Adjusted R-squared: 0.3783 F-statistic: 46.03 on 1 and 73 DF, p-value: 2.604e-09 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.06065 -0.15308 -0.01741 -0.00199 1.13398 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -15.37028 2.50350 -6.140 8.28e-10 *** x.var 0.23484 0.03509 6.693 2.19e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 177.5473 on 74 degrees of freedom Residual deviance: 9.5766 on 73 degrees of freedom AIC: 50.241 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- TUNISIA -- 22 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -4.5506 -2.4481 -0.4812 1.7554 15.0077 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.18126 0.84556 -3.762 0.000337 *** x.var 0.13565 0.01933 7.016 9.73e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.625 on 73 degrees of freedom Multiple R-squared: 0.4027, Adjusted R-squared: 0.3946 F-statistic: 49.22 on 1 and 73 DF, p-value: 9.733e-10 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.0556 -0.4878 -0.0120 0.4638 1.5273 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.693994 0.149833 -4.632 1.54e-05 *** x.var 0.030695 0.003426 8.959 2.22e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6423 on 73 degrees of freedom Multiple R-squared: 0.5237, Adjusted R-squared: 0.5172 F-statistic: 80.27 on 1 and 73 DF, p-value: 2.218e-13 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.29593 -0.29796 -0.06746 -0.01300 1.02090 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.99469 1.10041 -9.991 <2e-16 *** x.var 0.18982 0.01563 12.146 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 505.681 on 74 degrees of freedom Residual deviance: 15.995 on 73 degrees of freedom AIC: 85.249 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- TURKEY -- 574 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -99.41 -60.63 -17.85 33.70 423.38 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -70.659 22.087 -3.199 0.00204 ** x.var 2.950 0.505 5.842 1.33e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 94.68 on 73 degrees of freedom Multiple R-squared: 0.3186, Adjusted R-squared: 0.3092 F-statistic: 34.13 on 1 and 73 DF, p-value: 1.335e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.2465 -1.1108 -0.0439 1.0334 2.7312 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.539232 0.318229 -4.837 7.13e-06 *** x.var 0.068832 0.007276 9.459 2.57e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.364 on 73 degrees of freedom Multiple R-squared: 0.5507, Adjusted R-squared: 0.5446 F-statistic: 89.48 on 1 and 73 DF, p-value: 2.574e-14 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -3.7833 -0.8044 -0.0852 -0.0091 1.9663 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.68084 0.31161 -37.48 <2e-16 *** x.var 0.24250 0.00436 55.62 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 11934.66 on 74 degrees of freedom Residual deviance: 131.16 on 73 degrees of freedom AIC: 250.95 Number of Fisher Scoring iterations: 5 -------------------------------------------------------------------------------- UGANDA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- UKRAINE -- 37 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -5.729 -4.333 -1.155 2.275 26.281 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -4.87387 1.38531 -3.518 0.000752 *** x.var 0.20791 0.03168 6.564 6.64e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.939 on 73 degrees of freedom Multiple R-squared: 0.3711, Adjusted R-squared: 0.3625 F-statistic: 43.08 on 1 and 73 DF, p-value: 6.639e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.0869 -0.4799 -0.0725 0.4195 1.6581 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.80998 0.15174 -5.338 1.02e-06 *** x.var 0.03719 0.00347 10.720 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.6505 on 73 degrees of freedom Multiple R-squared: 0.6115, Adjusted R-squared: 0.6062 F-statistic: 114.9 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.91517 -0.23272 -0.05372 -0.01029 0.79308 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.51039 0.88449 -11.88 <2e-16 *** x.var 0.18902 0.01257 15.04 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 758.2166 on 74 degrees of freedom Residual deviance: 9.0699 on 73 degrees of freedom AIC: 97.3 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- UNITED ARAB EMIRATES -- 10 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.4309 -1.1320 -0.4650 0.7638 6.4457 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.64144 0.44248 -3.71 0.000402 *** x.var 0.07021 0.01012 6.94 1.35e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.897 on 73 degrees of freedom Multiple R-squared: 0.3975, Adjusted R-squared: 0.3892 F-statistic: 48.16 on 1 and 73 DF, p-value: 1.347e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.81395 -0.38793 0.03809 0.26500 1.21549 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.521681 0.116578 -4.475 2.76e-05 *** x.var 0.023028 0.002666 8.639 8.86e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4998 on 73 degrees of freedom Multiple R-squared: 0.5055, Adjusted R-squared: 0.4988 F-statistic: 74.63 on 1 and 73 DF, p-value: 8.863e-13 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.06435 -0.28053 -0.05598 -0.01120 1.29297 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -11.24431 1.47603 -7.618 2.58e-14 *** x.var 0.18407 0.02101 8.762 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 263.283 on 74 degrees of freedom Residual deviance: 13.243 on 73 degrees of freedom AIC: 71.14 Number of Fisher Scoring iterations: 6 -------------------------------------------------------------------------------- UNITED KINGDOM -- 4943 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -778.2 -552.2 -133.3 265.0 3631.9 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -609.288 186.515 -3.267 0.00166 ** x.var 25.605 4.265 6.004 6.86e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 799.6 on 73 degrees of freedom Multiple R-squared: 0.3306, Adjusted R-squared: 0.3214 F-statistic: 36.05 on 1 and 73 DF, p-value: 6.861e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.6406 -1.3692 0.0365 1.2332 2.1993 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.314582 0.341154 -6.785 2.61e-09 *** x.var 0.115238 0.007801 14.773 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.462 on 73 degrees of freedom Multiple R-squared: 0.7493, Adjusted R-squared: 0.7459 F-statistic: 218.2 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -6.9626 -1.7917 -0.3181 -0.0507 3.3890 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.108577 0.096168 -84.32 <2e-16 *** x.var 0.222823 0.001352 164.82 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 99276.08 on 74 degrees of freedom Residual deviance: 305.38 on 73 degrees of freedom AIC: 520.87 Number of Fisher Scoring iterations: 4 -------------------------------------------------------------------------------- URUGUAY -- 5 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.9667 -0.5456 -0.1246 0.2282 3.8740 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.558198 0.217971 -2.561 0.0125 * x.var 0.022760 0.004984 4.567 1.97e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.9344 on 73 degrees of freedom Multiple R-squared: 0.2222, Adjusted R-squared: 0.2115 F-statistic: 20.85 on 1 and 73 DF, p-value: 1.968e-05 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.41570 -0.23540 -0.05511 0.12519 1.30784 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.237270 0.084517 -2.807 0.0064 ** x.var 0.009746 0.001933 5.043 3.23e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3623 on 73 degrees of freedom Multiple R-squared: 0.2584, Adjusted R-squared: 0.2482 F-statistic: 25.43 on 1 and 73 DF, p-value: 3.229e-06 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.85306 -0.03973 -0.00126 -0.00004 0.98436 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -25.95104 5.68569 -4.564 5.01e-06 *** x.var 0.37224 0.07807 4.768 1.86e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 111.514 on 74 degrees of freedom Residual deviance: 4.632 on 73 degrees of freedom AIC: 30.738 Number of Fisher Scoring iterations: 9 -------------------------------------------------------------------------------- US -- 9619 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1565.2 -1078.5 -263.6 573.0 7023.1 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1206.738 368.403 -3.276 0.00161 ** x.var 50.702 8.424 6.019 6.45e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1579 on 73 degrees of freedom Multiple R-squared: 0.3317, Adjusted R-squared: 0.3225 F-statistic: 36.23 on 1 and 73 DF, p-value: 6.451e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -2.5550 -0.7694 -0.1987 1.1282 2.2885 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.419416 0.300577 -8.049 1.14e-11 *** x.var 0.130906 0.006873 19.047 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.289 on 73 degrees of freedom Multiple R-squared: 0.8325, Adjusted R-squared: 0.8302 F-statistic: 362.8 on 1 and 73 DF, p-value: < 2.2e-16 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -11.7049 -1.5308 -0.3524 -0.0447 5.5571 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.4720701 0.0685674 -109.0 <2e-16 *** x.var 0.2234744 0.0009637 231.9 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 196682.31 on 74 degrees of freedom Residual deviance: 473.24 on 73 degrees of freedom AIC: 735.09 Number of Fisher Scoring iterations: 4 -------------------------------------------------------------------------------- UZBEKISTAN -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.73111 -0.40375 -0.07638 0.24667 1.23349 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.419099 0.125649 -3.335 0.00134 ** x.var 0.017696 0.002873 6.159 3.61e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5386 on 73 degrees of freedom Multiple R-squared: 0.342, Adjusted R-squared: 0.3329 F-statistic: 37.94 on 1 and 73 DF, p-value: 3.609e-08 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.40662 -0.22467 -0.04273 0.13922 0.67233 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.232650 0.069341 -3.355 0.00126 ** x.var 0.009835 0.001586 6.203 3.01e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2973 on 73 degrees of freedom Multiple R-squared: 0.3452, Adjusted R-squared: 0.3362 F-statistic: 38.48 on 1 and 73 DF, p-value: 3.009e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.93690 -0.18326 -0.02606 -0.00372 1.31099 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -14.52034 3.43340 -4.229 2.35e-05 *** x.var 0.21072 0.04843 4.351 1.35e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 77.129 on 74 degrees of freedom Residual deviance: 10.309 on 73 degrees of freedom AIC: 39.833 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- VENEZUELA -- 7 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -1.5780 -0.8622 -0.1851 0.4533 5.1125 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.936937 0.316099 -2.964 0.0041 ** x.var 0.038691 0.007228 5.353 9.57e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.355 on 73 degrees of freedom Multiple R-squared: 0.2819, Adjusted R-squared: 0.2721 F-statistic: 28.66 on 1 and 73 DF, p-value: 9.571e-07 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.58728 -0.32265 -0.05801 0.19232 1.37772 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.342521 0.104386 -3.281 0.00159 ** x.var 0.014305 0.002387 5.993 7.17e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4475 on 73 degrees of freedom Multiple R-squared: 0.3298, Adjusted R-squared: 0.3206 F-statistic: 35.92 on 1 and 73 DF, p-value: 7.173e-08 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -1.06126 -0.09965 -0.00798 -0.00056 0.87769 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -19.27357 3.29014 -5.858 4.69e-09 *** x.var 0.28768 0.04564 6.303 2.92e-10 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 173.4315 on 74 degrees of freedom Residual deviance: 7.9797 on 73 degrees of freedom AIC: 43.086 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- VIETNAM -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- ZAMBIA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.18665 -0.11191 -0.03717 0.03756 0.80931 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.100180 0.048939 -2.047 0.044252 * x.var 0.004040 0.001119 3.610 0.000558 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2098 on 73 degrees of freedom Multiple R-squared: 0.1515, Adjusted R-squared: 0.1399 F-statistic: 13.03 on 1 and 73 DF, p-value: 0.0005579 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.12937 -0.07757 -0.02577 0.02604 0.56097 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0694396 0.0339217 -2.047 0.044252 * x.var 0.0028002 0.0007756 3.610 0.000558 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1454 on 73 degrees of freedom Multiple R-squared: 0.1515, Adjusted R-squared: 0.1399 F-statistic: 13.03 on 1 and 73 DF, p-value: 0.0005579 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.64399 -0.00743 -0.00007 0.00000 0.90341 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -37.8419 18.9841 -1.993 0.0462 * x.var 0.5108 0.2582 1.978 0.0479 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 23.4496 on 74 degrees of freedom Residual deviance: 2.3699 on 73 degrees of freedom AIC: 14.37 Number of Fisher Scoring iterations: 10 -------------------------------------------------------------------------------- ZIMBABWE -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.4661 -0.2413 -0.0166 0.2081 0.5218 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.274955 0.067979 -4.045 0.000129 *** x.var 0.012148 0.001554 7.815 3.13e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2914 on 73 degrees of freedom Multiple R-squared: 0.4555, Adjusted R-squared: 0.4481 F-statistic: 61.08 on 1 and 73 DF, p-value: 3.129e-11 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.3231 -0.1673 -0.0115 0.1443 0.3617 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.190584 0.047119 -4.045 0.000129 *** x.var 0.008420 0.001077 7.815 3.13e-11 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.202 on 73 degrees of freedom Multiple R-squared: 0.4555, Adjusted R-squared: 0.4481 F-statistic: 61.08 on 1 and 73 DF, p-value: 3.129e-11 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.70970 -0.23432 -0.06697 -0.01916 1.02614 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.10608 2.63831 -3.831 0.000128 *** x.var 0.14307 0.03832 3.734 0.000189 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 46.9961 on 74 degrees of freedom Residual deviance: 8.5674 on 73 degrees of freedom AIC: 40.567 Number of Fisher Scoring iterations: 7 -------------------------------------------------------------------------------- DOMINICA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- GRENADA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- MOZAMBIQUE -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- SYRIA -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.61747 -0.35115 -0.08484 0.18148 1.35374 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.347027 0.118114 -2.938 0.00442 ** x.var 0.014395 0.002701 5.330 1.05e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5063 on 73 degrees of freedom Multiple R-squared: 0.2802, Adjusted R-squared: 0.2703 F-statistic: 28.41 on 1 and 73 DF, p-value: 1.048e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.34466 -0.19610 -0.04754 0.10102 0.73789 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.193371 0.065328 -2.960 0.00415 ** x.var 0.008030 0.001494 5.376 8.74e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2801 on 73 degrees of freedom Multiple R-squared: 0.2836, Adjusted R-squared: 0.2738 F-statistic: 28.9 on 1 and 73 DF, p-value: 8.743e-07 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.91113 -0.10085 -0.00972 -0.00082 1.25277 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -18.77249 4.96780 -3.779 0.000158 *** x.var 0.26706 0.06916 3.862 0.000113 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 67.6913 on 74 degrees of freedom Residual deviance: 7.8638 on 73 degrees of freedom AIC: 32.16 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- TIMOR-LESTE -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- BELIZE -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- LAOS -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- LIBYA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.18665 -0.11191 -0.03717 0.03756 0.80931 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.100180 0.048939 -2.047 0.044252 * x.var 0.004040 0.001119 3.610 0.000558 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2098 on 73 degrees of freedom Multiple R-squared: 0.1515, Adjusted R-squared: 0.1399 F-statistic: 13.03 on 1 and 73 DF, p-value: 0.0005579 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.12937 -0.07757 -0.02577 0.02604 0.56097 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0694396 0.0339217 -2.047 0.044252 * x.var 0.0028002 0.0007756 3.610 0.000558 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1454 on 73 degrees of freedom Multiple R-squared: 0.1515, Adjusted R-squared: 0.1399 F-statistic: 13.03 on 1 and 73 DF, p-value: 0.0005579 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.64399 -0.00743 -0.00007 0.00000 0.90341 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -37.8419 18.9841 -1.993 0.0462 * x.var 0.5108 0.2582 1.978 0.0479 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 23.4496 on 74 degrees of freedom Residual deviance: 2.3699 on 73 degrees of freedom AIC: 14.37 Number of Fisher Scoring iterations: 10 -------------------------------------------------------------------------------- WEST BANK AND GAZA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.40704 -0.22177 -0.03651 0.14875 0.58295 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.233874 0.066101 -3.538 0.000705 *** x.var 0.010014 0.001511 6.626 5.11e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2834 on 73 degrees of freedom Multiple R-squared: 0.3755, Adjusted R-squared: 0.367 F-statistic: 43.9 on 1 and 73 DF, p-value: 5.11e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.28214 -0.15372 -0.02531 0.10311 0.40407 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.162109 0.045817 -3.538 0.000705 *** x.var 0.006941 0.001048 6.626 5.11e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1964 on 73 degrees of freedom Multiple R-squared: 0.3755, Adjusted R-squared: 0.367 F-statistic: 43.9 on 1 and 73 DF, p-value: 5.11e-09 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.70249 -0.17118 -0.03469 -0.00643 1.01308 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -13.06778 3.86551 -3.381 0.000723 *** x.var 0.18232 0.05505 3.312 0.000927 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 42.2310 on 74 degrees of freedom Residual deviance: 6.7204 on 73 degrees of freedom AIC: 32.72 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- GUINEA-BISSAU -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- MALI -- 5 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.9162 -0.5189 -0.1215 0.2544 3.9119 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.522883 0.187829 -2.784 0.00684 ** x.var 0.021479 0.004295 5.001 3.8e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8052 on 73 degrees of freedom Multiple R-squared: 0.2552, Adjusted R-squared: 0.245 F-statistic: 25.01 on 1 and 73 DF, p-value: 3.796e-06 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.42316 -0.24020 -0.05725 0.12570 1.28949 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.239431 0.081723 -2.930 0.00452 ** x.var 0.009889 0.001869 5.292 1.22e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3503 on 73 degrees of freedom Multiple R-squared: 0.2773, Adjusted R-squared: 0.2674 F-statistic: 28.01 on 1 and 73 DF, p-value: 1.218e-06 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.97311 -0.08373 -0.00445 -0.00024 1.00635 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -21.98074 4.91130 -4.476 7.62e-06 *** x.var 0.31691 0.06785 4.671 3.00e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 101.9699 on 74 degrees of freedom Residual deviance: 6.7459 on 73 degrees of freedom AIC: 33.421 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- SAINT KITTS AND NEVIS -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- KOSOVO -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.40704 -0.22177 -0.03651 0.14875 0.58295 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.233874 0.066101 -3.538 0.000705 *** x.var 0.010014 0.001511 6.626 5.11e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2834 on 73 degrees of freedom Multiple R-squared: 0.3755, Adjusted R-squared: 0.367 F-statistic: 43.9 on 1 and 73 DF, p-value: 5.11e-09 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.28214 -0.15372 -0.02531 0.10311 0.40407 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.162109 0.045817 -3.538 0.000705 *** x.var 0.006941 0.001048 6.626 5.11e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1964 on 73 degrees of freedom Multiple R-squared: 0.3755, Adjusted R-squared: 0.367 F-statistic: 43.9 on 1 and 73 DF, p-value: 5.11e-09 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.70249 -0.17118 -0.03469 -0.00643 1.01308 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -13.06778 3.86551 -3.381 0.000723 *** x.var 0.18232 0.05505 3.312 0.000927 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 42.2310 on 74 degrees of freedom Residual deviance: 6.7204 on 73 degrees of freedom AIC: 32.72 Number of Fisher Scoring iterations: 8 -------------------------------------------------------------------------------- BURMA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.26256 -0.15361 -0.04467 0.06428 0.73155 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.143784 0.056621 -2.539 0.0132 * x.var 0.005889 0.001295 4.549 2.1e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2427 on 73 degrees of freedom Multiple R-squared: 0.2208, Adjusted R-squared: 0.2102 F-statistic: 20.69 on 1 and 73 DF, p-value: 2.102e-05 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.18199 -0.10648 -0.03096 0.04456 0.50707 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0996633 0.0392468 -2.539 0.0132 * x.var 0.0040820 0.0008974 4.549 2.1e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1682 on 73 degrees of freedom Multiple R-squared: 0.2208, Adjusted R-squared: 0.2102 F-statistic: 20.69 on 1 and 73 DF, p-value: 2.102e-05 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.67480 -0.04218 -0.00187 -0.00008 0.96136 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -24.6964 10.0142 -2.466 0.0137 * x.var 0.3365 0.1380 2.438 0.0148 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 30.3087 on 74 degrees of freedom Residual deviance: 3.6262 on 73 degrees of freedom AIC: 19.626 Number of Fisher Scoring iterations: 9 -------------------------------------------------------------------------------- MS ZAANDAM -- 2 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.45197 -0.26776 -0.08355 0.10066 1.53807 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.245045 0.106379 -2.304 0.024100 * x.var 0.009957 0.002432 4.094 0.000108 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.456 on 73 degrees of freedom Multiple R-squared: 0.1867, Adjusted R-squared: 0.1756 F-statistic: 16.76 on 1 and 73 DF, p-value: 0.0001084 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.24827 -0.14708 -0.04589 0.05530 0.84487 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.134605 0.058434 -2.304 0.024100 * x.var 0.005470 0.001336 4.094 0.000108 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2505 on 73 degrees of freedom Multiple R-squared: 0.1867, Adjusted R-squared: 0.1756 F-statistic: 16.76 on 1 and 73 DF, p-value: 0.0001084 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.93697 -0.03320 -0.00078 -0.00002 1.32703 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -29.2059 9.4295 -3.097 0.00195 ** x.var 0.4055 0.1291 3.141 0.00169 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 54.1610 on 74 degrees of freedom Residual deviance: 6.0021 on 73 degrees of freedom AIC: 23.071 Number of Fisher Scoring iterations: 9 -------------------------------------------------------------------------------- BOTSWANA -- 1 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.26256 -0.15361 -0.04467 0.06428 0.73155 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.143784 0.056621 -2.539 0.0132 * x.var 0.005889 0.001295 4.549 2.1e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2427 on 73 degrees of freedom Multiple R-squared: 0.2208, Adjusted R-squared: 0.2102 F-statistic: 20.69 on 1 and 73 DF, p-value: 2.102e-05 -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max -0.18199 -0.10648 -0.03096 0.04456 0.50707 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0996633 0.0392468 -2.539 0.0132 * x.var 0.0040820 0.0008974 4.549 2.1e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.1682 on 73 degrees of freedom Multiple R-squared: 0.2208, Adjusted R-squared: 0.2102 F-statistic: 20.69 on 1 and 73 DF, p-value: 2.102e-05 -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -0.67480 -0.04218 -0.00187 -0.00008 0.96136 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -24.6964 10.0142 -2.466 0.0137 * x.var 0.3365 0.1380 2.438 0.0148 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 30.3087 on 74 degrees of freedom Residual deviance: 3.6262 on 73 degrees of freedom AIC: 19.626 Number of Fisher Scoring iterations: 9 -------------------------------------------------------------------------------- BURUNDI -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- SIERRA LEONE -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- MALAWI -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- SOUTH SUDAN -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- WESTERN SAHARA -- 0 =============================== running models...=============================== Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- Linear Regression (lm): Call: lm(formula = y.var ~ x.var) Residuals: Min 1Q Median 3Q Max 0 0 0 0 0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA x.var 0 0 NA NA Residual standard error: 0 on 73 degrees of freedom Multiple R-squared: NaN, Adjusted R-squared: NaN F-statistic: NaN on 1 and 73 DF, p-value: NA -------------------------------------------------------------------------------- GLM using Family [1] "poisson" : Call: glm(formula = y.var ~ x.var, family = family) Deviance Residuals: Min 1Q Median 3Q Max -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 -2.748e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.630e+01 7.282e+04 0 1 x.var 7.326e-16 1.665e+03 0 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 0.0000e+00 on 74 degrees of freedom Residual deviance: 5.6627e-10 on 73 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 24 -------------------------------------------------------------------------------- [1] "covid19.ALL.agg.cases" "covid19.confirmed.cases" "covid19.TS.deaths" [4] "covid19data" [1] "covid19data_5April2020" [1] "C:/Users/Seraya/Desktop/resCovid19/datasets" [1] "covid19data_5April2020" NULL 1 function (x, ...) 2 UseMethod("head") $aggregated FIPS Admin2 Province_State Country_Region Last_Update Lat Long_ 1 45001 Abbeville South Carolina US 2020-04-05 23:06:45 34.22333 -82.46171 2 22001 Acadia Louisiana US 2020-04-05 23:06:45 30.29506 -92.41420 3 51001 Accomack Virginia US 2020-04-05 23:06:45 37.76707 -75.63235 4 16001 Ada Idaho US 2020-04-05 23:06:45 43.45266 -116.24155 5 19001 Adair Iowa US 2020-04-05 23:06:45 41.33076 -94.47106 6 21001 Adair Kentucky US 2020-04-05 23:06:45 37.10460 -85.28130 7 29001 Adair Missouri US 2020-04-05 23:06:45 40.19059 -92.60078 8 40001 Adair Oklahoma US 2020-04-05 23:06:45 35.88494 -94.65859 9 8001 Adams Colorado US 2020-04-05 23:06:45 39.87432 -104.33626 10 16003 Adams Idaho US 2020-04-05 23:06:45 44.89334 -116.45452 11 17001 Adams Illinois US 2020-04-05 23:06:45 39.98816 -91.18787 12 18001 Adams Indiana US 2020-04-05 23:06:45 40.74577 -84.93671 13 28001 Adams Mississippi US 2020-04-05 23:06:45 31.47670 -91.35326 14 31001 Adams Nebraska US 2020-04-05 23:06:45 40.52449 -98.50118 15 39001 Adams Ohio US 2020-04-05 23:06:45 38.84541 -83.47190 16 42001 Adams Pennsylvania US 2020-04-05 23:06:45 39.87140 -77.21610 17 53001 Adams Washington US 2020-04-05 23:06:45 46.98300 -118.56017 18 55001 Adams Wisconsin US 2020-04-05 23:06:45 43.96975 -89.76783 19 50001 Addison Vermont US 2020-04-05 23:06:45 44.03217 -73.14131 20 45003 Aiken South Carolina US 2020-04-05 23:06:45 33.54338 -81.63645 21 12001 Alachua Florida US 2020-04-05 23:06:45 29.67867 -82.35928 22 37001 Alamance North Carolina US 2020-04-05 23:06:45 36.04347 -79.39976 23 6001 Alameda California US 2020-04-05 23:06:45 37.64629 -121.89293 24 8003 Alamosa Colorado US 2020-04-05 23:06:45 37.57251 -105.78855 25 36001 Albany New York US 2020-04-05 23:06:45 42.60060 -73.97724 26 56001 Albany Wyoming US 2020-04-05 23:06:45 41.65499 -105.72354 27 51003 Albemarle Virginia US 2020-04-05 23:06:45 38.02081 -78.55481 28 28003 Alcorn Mississippi US 2020-04-05 23:06:45 34.88084 -88.57996 29 37003 Alexander North Carolina US 2020-04-05 23:06:45 35.92238 -81.17752 30 51510 Alexandria Virginia US 2020-04-05 23:06:45 38.81400 -77.08183 31 19005 Allamakee Iowa US 2020-04-05 23:06:45 43.28383 -91.37861 32 26005 Allegan Michigan US 2020-04-05 23:06:45 42.59147 -85.89103 33 24001 Allegany Maryland US 2020-04-05 23:06:45 39.62358 -78.69280 34 36003 Allegany New York US 2020-04-05 23:06:45 42.25748 -78.02750 35 37005 Alleghany North Carolina US 2020-04-05 23:06:45 36.49361 -81.12857 36 51005 Alleghany Virginia US 2020-04-05 23:06:45 37.78636 -80.00222 37 42003 Allegheny Pennsylvania US 2020-04-05 23:06:45 40.46810 -79.98168 38 18003 Allen Indiana US 2020-04-05 23:06:45 41.09194 -85.06801 39 21003 Allen Kentucky US 2020-04-05 23:06:45 36.75198 -86.19458 40 22003 Allen Louisiana US 2020-04-05 23:06:45 30.65386 -92.82442 41 39003 Allen Ohio US 2020-04-05 23:06:45 40.77285 -84.10802 42 45005 Allendale South Carolina US 2020-04-05 23:06:45 32.98837 -81.35321 43 6003 Alpine California US 2020-04-05 23:06:45 38.59679 -119.82236 44 6005 Amador California US 2020-04-05 23:06:45 38.44583 -120.65696 45 51007 Amelia Virginia US 2020-04-05 23:06:45 37.34081 -77.98585 46 51009 Amherst Virginia US 2020-04-05 23:06:45 37.60308 -79.14549 47 28005 Amite Mississippi US 2020-04-05 23:06:45 31.17467 -90.80502 48 2020 Anchorage Alaska US 2020-04-05 23:06:45 61.14998 -149.14270 49 20003 Anderson Kansas US 2020-04-05 23:06:45 38.21413 -95.29273 50 21005 Anderson Kentucky US 2020-04-05 23:06:45 38.00671 -84.99172 51 45007 Anderson South Carolina US 2020-04-05 23:06:45 34.51828 -82.63960 52 47001 Anderson Tennessee US 2020-04-05 23:06:45 36.12684 -84.19966 53 48001 Anderson Texas US 2020-04-05 23:06:45 31.81535 -95.65355 54 48003 Andrews Texas US 2020-04-05 23:06:45 32.30469 -102.63765 55 23001 Androscoggin Maine US 2020-04-05 23:06:45 44.16647 -70.20381 56 48005 Angelina Texas US 2020-04-05 23:06:45 31.25457 -94.60901 57 24003 Anne Arundel Maryland US 2020-04-05 23:06:45 39.00670 -76.60329 58 27003 Anoka Minnesota US 2020-04-05 23:06:45 45.27476 -93.24605 59 37007 Anson North Carolina US 2020-04-05 23:06:45 34.97403 -80.09953 60 31003 Antelope Nebraska US 2020-04-05 23:06:45 42.17696 -98.06663 61 26009 Antrim Michigan US 2020-04-05 23:06:45 44.99690 -85.15503 62 4001 Apache Arizona US 2020-04-05 23:06:45 35.39465 -109.48924 63 19007 Appanoose Iowa US 2020-04-05 23:06:45 40.74324 -92.86866 64 13001 Appling Georgia US 2020-04-05 23:06:45 31.74847 -82.28909 65 51011 Appomattox Virginia US 2020-04-05 23:06:45 37.37570 -78.81340 66 48007 Aransas Texas US 2020-04-05 23:06:45 28.10556 -96.99950 67 8005 Arapahoe Colorado US 2020-04-05 23:06:45 39.64977 -104.33536 68 8007 Archuleta Colorado US 2020-04-05 23:06:45 37.19474 -107.04769 69 26011 Arenac Michigan US 2020-04-05 23:06:45 44.06363 -83.89278 70 5001 Arkansas Arkansas US 2020-04-05 23:06:45 34.29145 -91.37277 71 51013 Arlington Virginia US 2020-04-05 23:06:45 38.87677 -77.10140 72 42005 Armstrong Pennsylvania US 2020-04-05 23:06:45 40.81666 -79.46291 73 23003 Aroostook Maine US 2020-04-05 23:06:45 46.65926 -68.59841 74 22005 Ascension Louisiana US 2020-04-05 23:06:45 30.20406 -90.91328 75 37009 Ashe North Carolina US 2020-04-05 23:06:45 36.43296 -81.49863 76 39005 Ashland Ohio US 2020-04-05 23:06:45 40.84772 -82.27281 77 55003 Ashland Wisconsin US 2020-04-05 23:06:45 46.31957 -90.67837 78 5003 Ashley Arkansas US 2020-04-05 23:06:45 33.19153 -91.76985 79 39007 Ashtabula Ohio US 2020-04-05 23:06:45 41.70860 -80.74830 80 53003 Asotin Washington US 2020-04-05 23:06:45 46.18894 -117.20229 81 22007 Assumption Louisiana US 2020-04-05 23:06:45 29.89946 -91.06462 82 48013 Atascosa Texas US 2020-04-05 23:06:45 28.89333 -98.52730 83 20005 Atchison Kansas US 2020-04-05 23:06:45 39.53186 -95.30870 Confirmed Deaths Recovered Active Combined_Key 1 6 0 0 0 Abbeville, South Carolina, US 2 75 2 0 0 Acadia, Louisiana, US 3 11 0 0 0 Accomack, Virginia, US 4 385 3 0 0 Ada, Idaho, US 5 1 0 0 0 Adair, Iowa, US 6 3 0 0 0 Adair, Kentucky, US 7 10 0 0 0 Adair, Missouri, US 8 18 0 0 0 Adair, Oklahoma, US 9 354 10 0 0 Adams, Colorado, US 10 1 0 0 0 Adams, Idaho, US 11 3 0 0 0 Adams, Illinois, US 12 2 0 0 0 Adams, Indiana, US 13 19 0 0 0 Adams, Mississippi, US 14 11 0 0 0 Adams, Nebraska, US 15 2 0 0 0 Adams,Ohio,US 16 22 0 0 0 Adams, Pennsylvania, US 17 23 0 0 0 Adams, Washington, US 18 1 0 0 0 Adams, Wisconsin, US 19 40 0 0 0 Addison, Vermont, US 20 27 1 0 0 Aiken, South Carolina, US 21 123 0 0 0 Alachua, Florida, US 22 19 0 0 0 Alamance, North Carolina, US 23 566 12 0 0 Alameda, California, US 24 4 0 0 0 Alamosa, Colorado, US 25 305 8 0 0 Albany, New York, US 26 4 0 0 0 Albany, Wyoming, US 27 32 0 0 0 Albemarle, Virginia, US 28 6 0 0 0 Alcorn, Mississippi, US 29 2 0 0 0 Alexander, North Carolina, US 30 74 0 0 0 Alexandria, Virginia, US 31 17 1 0 0 Allamakee, Iowa, US 32 14 0 0 0 Allegan, Michigan, US 33 6 0 0 0 Allegany, Maryland, US 34 16 1 0 0 Allegany, New York, US 35 2 0 0 0 Alleghany, North Carolina, US 36 2 0 0 0 Alleghany, Virginia, US 37 605 4 0 0 Allegheny, Pennsylvania, US 38 63 2 0 0 Allen, Indiana, US 39 2 0 0 0 Allen, Kentucky, US 40 40 5 0 0 Allen, Louisiana, US 41 18 0 0 0 Allen, Ohio, US 42 2 0 0 0 Allendale, South Carolina, US 43 1 0 0 0 Alpine, California, US 44 0 0 0 0 Amador, California, US 45 6 0 0 0 Amelia, Virginia, US 46 6 0 0 0 Amherst, Virginia, US 47 6 1 0 0 Amite, Mississippi, US 48 85 1 0 0 Anchorage, Alaska, US 49 0 0 0 0 Anderson, Kansas, US 50 3 1 0 0 Anderson, Kentucky, US 51 69 3 0 0 Anderson, South Carolina, US 52 10 0 0 0 Anderson, Tennessee, US 53 1 0 0 0 Anderson, Texas, US 54 6 0 0 0 Andrews, Texas, US 55 20 0 0 0 Androscoggin, Maine, US 56 10 0 0 0 Angelina, Texas, US 57 319 6 0 0 Anne Arundel, Maryland, US 58 36 0 0 0 Anoka, Minnesota, US 59 5 0 0 0 Anson, North Carolina, US 60 1 0 0 0 Antelope, Nebraska, US 61 4 0 0 0 Antrim, Michigan, US 62 45 0 0 0 Apache,Arizona,US 63 1 1 0 0 Appanoose, Iowa, US 64 5 0 0 0 Appling, Georgia, US 65 0 0 0 0 Appomattox, Virginia, US 66 1 0 0 0 Aransas, Texas, US 67 608 13 0 0 Arapahoe, Colorado, US 68 1 0 0 0 Archuleta, Colorado, US 69 3 0 0 0 Arenac, Michigan, US 70 1 0 0 0 Arkansas, Arkansas, US 71 181 2 0 0 Arlington, Virginia, US 72 12 0 0 0 Armstrong, Pennsylvania, US 73 1 0 0 0 Aroostook, Maine, US 74 286 11 0 0 Ascension, Louisiana, US 75 1 0 0 0 Ashe, North Carolina, US 76 3 0 0 0 Ashland, Ohio, US 77 1 0 0 0 Ashland, Wisconsin, US 78 1 0 0 0 Ashley, Arkansas, US 79 12 0 0 0 Ashtabula, Ohio, US 80 2 0 0 0 Asotin, Washington, US 81 59 0 0 0 Assumption, Louisiana, US 82 2 0 0 0 Atascosa, Texas, US 83 2 0 0 0 Atchison, Kansas, US [ reached 'max' / getOption("max.print") -- omitted 2681 rows ] $time.series Province.State Country.Region Lat Long 2020-01-22 2020-01-23 1 Afghanistan 33.0000 65.0000 0 0 2 Albania 41.1533 20.1683 0 0 3 Algeria 28.0339 1.6596 0 0 4 Andorra 42.5063 1.5218 0 0 5 Angola -11.2027 17.8739 0 0 6 Antigua and Barbuda 17.0608 -61.7964 0 0 7 Argentina -38.4161 -63.6167 0 0 8 Armenia 40.0691 45.0382 0 0 9 Australian Capital Territory Australia -35.4735 149.0124 0 0 10 New South Wales Australia -33.8688 151.2093 0 0 11 Northern Territory Australia -12.4634 130.8456 0 0 12 Queensland Australia -28.0167 153.4000 0 0 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 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84 115 9 0 1 1 1 2 2 3 4 10 65 92 112 134 171 210 267 307 11 1 1 1 1 1 1 1 1 12 20 35 46 61 68 78 94 144 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 1 24 24 40 40 74 84 94 110 2 70 76 89 104 123 146 174 186 3 90 139 201 230 264 302 367 409 4 75 88 113 133 164 188 224 267 5 1 2 2 3 3 3 4 4 6 1 1 1 3 3 3 7 7 7 128 158 266 301 387 387 502 589 8 136 160 194 235 249 265 290 329 9 6 9 19 32 39 39 53 62 10 353 436 669 669 818 1029 1219 1405 11 3 3 5 5 6 6 12 12 12 184 221 259 319 397 443 493 555 2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 1 110 120 170 174 237 273 281 299 2 197 212 223 243 259 277 304 333 3 454 511 584 716 847 986 1171 1251 4 308 334 370 376 390 428 439 466 5 5 7 7 7 8 8 8 10 6 7 7 7 7 7 9 15 15 7 690 745 820 1054 1054 1133 1265 1451 8 407 424 482 532 571 663 736 770 9 71 77 78 80 84 87 91 93 10 1617 1791 2032 2032 2182 2298 2389 2493 11 15 15 15 17 19 21 22 26 12 625 656 689 743 781 835 873 900 2020-04-05 status 1 349 confirmed 2 361 confirmed 3 1320 confirmed 4 501 confirmed 5 14 confirmed 6 15 confirmed 7 1451 confirmed 8 822 confirmed 9 96 confirmed 10 2580 confirmed 11 27 confirmed 12 907 confirmed [ reached 'max' / getOption("max.print") -- omitted 760 rows ] $ts.dep Province.State Country.Region Lat Long 2020-01-22 2020-01-23 2020-01-24 1 Thailand 15.0000 101.0000 2 3 5 2 Japan 36.0000 138.0000 2 1 2 3 Singapore 1.2833 103.8333 0 1 3 4 Nepal 28.1667 84.2500 0 0 0 5 Malaysia 2.5000 112.5000 0 0 0 6 British Columbia Canada 49.2827 -123.1207 0 0 0 7 New South Wales Australia -33.8688 151.2093 0 0 0 8 Victoria Australia -37.8136 144.9631 0 0 0 9 Queensland Australia -28.0167 153.4000 0 0 0 10 Cambodia 11.5500 104.9167 0 0 0 11 Sri Lanka 7.0000 81.0000 0 0 0 12 Germany 51.0000 9.0000 0 0 0 13 Finland 64.0000 26.0000 0 0 0 14 United Arab Emirates 24.0000 54.0000 0 0 0 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 1 7 8 8 14 14 14 19 19 2 2 4 4 7 7 11 15 20 3 3 4 5 7 7 10 13 16 4 1 1 1 1 1 1 1 1 5 3 4 4 4 7 8 8 8 6 0 0 0 1 1 1 1 1 7 0 3 4 4 4 4 4 4 8 0 1 1 1 1 2 3 4 9 0 0 0 0 1 3 2 3 10 0 0 1 1 1 1 1 1 11 0 0 1 1 1 1 1 1 12 0 0 1 4 4 4 5 8 13 0 0 0 0 1 1 1 1 14 0 0 0 0 4 4 4 4 2020-02-02 2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 1 19 19 25 25 25 25 32 32 2 20 20 22 22 45 25 25 26 3 18 18 24 28 28 30 33 40 4 1 1 1 1 1 1 1 1 5 8 8 10 12 12 12 16 16 6 1 1 1 2 2 4 4 4 7 4 4 4 4 4 4 4 4 8 4 4 4 4 4 4 4 4 9 2 2 3 3 4 5 5 5 10 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 12 10 12 12 12 12 13 13 14 13 1 1 1 1 1 1 1 1 14 5 5 5 5 5 5 7 7 2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 1 32 33 33 33 33 33 34 35 2 26 26 28 28 29 43 59 66 3 45 47 50 58 67 72 75 77 4 1 1 1 1 1 1 1 1 5 18 18 18 19 19 22 22 22 6 4 4 4 4 4 4 4 5 7 4 4 4 4 4 4 4 4 8 4 4 4 4 4 4 4 4 9 5 5 5 5 5 5 5 5 10 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 12 14 16 16 16 16 16 16 16 13 1 1 1 1 1 1 1 1 14 8 8 8 8 8 8 9 9 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25 1 35 35 35 35 35 35 35 37 2 74 84 94 105 122 147 159 170 3 81 84 84 85 85 89 89 91 4 1 1 1 1 1 1 1 1 5 22 22 22 22 22 22 22 22 6 5 5 5 6 6 6 6 7 7 4 4 4 4 4 4 4 4 8 4 4 4 4 4 4 4 4 9 5 5 5 5 5 5 5 5 10 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 12 16 16 16 16 16 16 16 17 13 1 1 1 1 1 1 1 1 14 9 9 9 9 13 13 13 13 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04 1 40 40 41 42 42 43 43 43 2 189 214 228 241 256 274 293 331 3 93 93 93 102 106 108 110 110 4 1 1 1 1 1 1 1 1 5 22 23 23 25 29 29 36 50 6 7 7 7 8 8 8 9 12 7 4 4 4 4 6 6 13 22 8 4 4 4 7 7 9 9 10 9 5 5 5 9 9 9 11 11 10 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 12 27 46 48 79 130 159 196 262 13 2 2 2 3 6 6 6 6 14 13 13 19 21 21 21 27 27 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11 2020-03-12 1 47 48 50 50 50 53 59 70 2 360 420 461 502 511 581 639 639 3 117 130 138 150 150 160 178 178 4 1 1 1 1 1 1 1 1 5 50 83 93 99 117 129 149 149 6 13 21 21 27 32 32 39 46 7 22 26 28 38 48 55 65 65 8 10 10 11 11 15 18 21 21 9 13 13 13 15 15 18 20 20 10 1 1 1 2 2 2 3 3 11 1 1 1 1 1 1 2 2 12 482 670 799 1040 1176 1457 1908 2078 13 12 15 15 23 30 40 59 59 14 29 29 45 45 45 74 74 85 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 1 75 82 114 147 177 212 272 322 2 701 773 839 825 878 889 924 963 3 200 212 226 243 266 313 345 385 4 1 1 1 1 1 1 1 1 5 197 238 428 566 673 790 900 1030 6 64 64 73 103 103 186 231 271 7 92 112 134 171 210 267 307 353 8 36 49 57 71 94 121 121 121 9 35 46 61 68 78 94 144 184 10 5 7 7 7 33 35 37 51 11 6 10 18 28 44 51 60 73 12 3675 4585 5795 7272 9257 12327 15320 19848 13 155 225 244 277 321 336 400 450 14 85 85 98 98 98 113 140 140 2020-03-21 2020-03-22 2020-03-23 status 1 411 599 599 confirmed 2 1007 1086 1086 confirmed 3 432 455 455 confirmed 4 1 2 2 confirmed 5 1183 1306 1306 confirmed 6 424 424 424 confirmed 7 436 533 533 confirmed 8 229 296 296 confirmed 9 221 221 221 confirmed 10 53 84 84 confirmed 11 77 82 82 confirmed 12 22213 24873 24873 confirmed 13 523 626 626 confirmed 14 153 153 153 confirmed [ reached 'max' / getOption("max.print") -- omitted 1489 rows ] NULL $aggregated FIPS Admin2 Province_State Country_Region Last_Update Lat Long_ 1 45001 Abbeville South Carolina US 2020-04-05 23:06:45 34.22333 -82.46171 2 22001 Acadia Louisiana US 2020-04-05 23:06:45 30.29506 -92.41420 3 51001 Accomack Virginia US 2020-04-05 23:06:45 37.76707 -75.63235 4 16001 Ada Idaho US 2020-04-05 23:06:45 43.45266 -116.24155 5 19001 Adair Iowa US 2020-04-05 23:06:45 41.33076 -94.47106 6 21001 Adair Kentucky US 2020-04-05 23:06:45 37.10460 -85.28130 7 29001 Adair Missouri US 2020-04-05 23:06:45 40.19059 -92.60078 8 40001 Adair Oklahoma US 2020-04-05 23:06:45 35.88494 -94.65859 9 8001 Adams Colorado US 2020-04-05 23:06:45 39.87432 -104.33626 10 16003 Adams Idaho US 2020-04-05 23:06:45 44.89334 -116.45452 11 17001 Adams Illinois US 2020-04-05 23:06:45 39.98816 -91.18787 12 18001 Adams Indiana US 2020-04-05 23:06:45 40.74577 -84.93671 13 28001 Adams Mississippi US 2020-04-05 23:06:45 31.47670 -91.35326 14 31001 Adams Nebraska US 2020-04-05 23:06:45 40.52449 -98.50118 15 39001 Adams Ohio US 2020-04-05 23:06:45 38.84541 -83.47190 16 42001 Adams Pennsylvania US 2020-04-05 23:06:45 39.87140 -77.21610 17 53001 Adams Washington US 2020-04-05 23:06:45 46.98300 -118.56017 18 55001 Adams Wisconsin US 2020-04-05 23:06:45 43.96975 -89.76783 19 50001 Addison Vermont US 2020-04-05 23:06:45 44.03217 -73.14131 20 45003 Aiken South Carolina US 2020-04-05 23:06:45 33.54338 -81.63645 21 12001 Alachua Florida US 2020-04-05 23:06:45 29.67867 -82.35928 22 37001 Alamance North Carolina US 2020-04-05 23:06:45 36.04347 -79.39976 23 6001 Alameda California US 2020-04-05 23:06:45 37.64629 -121.89293 24 8003 Alamosa Colorado US 2020-04-05 23:06:45 37.57251 -105.78855 25 36001 Albany New York US 2020-04-05 23:06:45 42.60060 -73.97724 26 56001 Albany Wyoming US 2020-04-05 23:06:45 41.65499 -105.72354 27 51003 Albemarle Virginia US 2020-04-05 23:06:45 38.02081 -78.55481 28 28003 Alcorn Mississippi US 2020-04-05 23:06:45 34.88084 -88.57996 29 37003 Alexander North Carolina US 2020-04-05 23:06:45 35.92238 -81.17752 30 51510 Alexandria Virginia US 2020-04-05 23:06:45 38.81400 -77.08183 31 19005 Allamakee Iowa US 2020-04-05 23:06:45 43.28383 -91.37861 32 26005 Allegan Michigan US 2020-04-05 23:06:45 42.59147 -85.89103 33 24001 Allegany Maryland US 2020-04-05 23:06:45 39.62358 -78.69280 34 36003 Allegany New York US 2020-04-05 23:06:45 42.25748 -78.02750 35 37005 Alleghany North Carolina US 2020-04-05 23:06:45 36.49361 -81.12857 36 51005 Alleghany Virginia US 2020-04-05 23:06:45 37.78636 -80.00222 37 42003 Allegheny Pennsylvania US 2020-04-05 23:06:45 40.46810 -79.98168 38 18003 Allen Indiana US 2020-04-05 23:06:45 41.09194 -85.06801 39 21003 Allen Kentucky US 2020-04-05 23:06:45 36.75198 -86.19458 40 22003 Allen Louisiana US 2020-04-05 23:06:45 30.65386 -92.82442 41 39003 Allen Ohio US 2020-04-05 23:06:45 40.77285 -84.10802 42 45005 Allendale South Carolina US 2020-04-05 23:06:45 32.98837 -81.35321 43 6003 Alpine California US 2020-04-05 23:06:45 38.59679 -119.82236 44 6005 Amador California US 2020-04-05 23:06:45 38.44583 -120.65696 45 51007 Amelia Virginia US 2020-04-05 23:06:45 37.34081 -77.98585 46 51009 Amherst Virginia US 2020-04-05 23:06:45 37.60308 -79.14549 47 28005 Amite Mississippi US 2020-04-05 23:06:45 31.17467 -90.80502 48 2020 Anchorage Alaska US 2020-04-05 23:06:45 61.14998 -149.14270 49 20003 Anderson Kansas US 2020-04-05 23:06:45 38.21413 -95.29273 50 21005 Anderson Kentucky US 2020-04-05 23:06:45 38.00671 -84.99172 51 45007 Anderson South Carolina US 2020-04-05 23:06:45 34.51828 -82.63960 52 47001 Anderson Tennessee US 2020-04-05 23:06:45 36.12684 -84.19966 53 48001 Anderson Texas US 2020-04-05 23:06:45 31.81535 -95.65355 54 48003 Andrews Texas US 2020-04-05 23:06:45 32.30469 -102.63765 55 23001 Androscoggin Maine US 2020-04-05 23:06:45 44.16647 -70.20381 56 48005 Angelina Texas US 2020-04-05 23:06:45 31.25457 -94.60901 57 24003 Anne Arundel Maryland US 2020-04-05 23:06:45 39.00670 -76.60329 58 27003 Anoka Minnesota US 2020-04-05 23:06:45 45.27476 -93.24605 59 37007 Anson North Carolina US 2020-04-05 23:06:45 34.97403 -80.09953 60 31003 Antelope Nebraska US 2020-04-05 23:06:45 42.17696 -98.06663 61 26009 Antrim Michigan US 2020-04-05 23:06:45 44.99690 -85.15503 62 4001 Apache Arizona US 2020-04-05 23:06:45 35.39465 -109.48924 63 19007 Appanoose Iowa US 2020-04-05 23:06:45 40.74324 -92.86866 64 13001 Appling Georgia US 2020-04-05 23:06:45 31.74847 -82.28909 65 51011 Appomattox Virginia US 2020-04-05 23:06:45 37.37570 -78.81340 66 48007 Aransas Texas US 2020-04-05 23:06:45 28.10556 -96.99950 67 8005 Arapahoe Colorado US 2020-04-05 23:06:45 39.64977 -104.33536 68 8007 Archuleta Colorado US 2020-04-05 23:06:45 37.19474 -107.04769 69 26011 Arenac Michigan US 2020-04-05 23:06:45 44.06363 -83.89278 70 5001 Arkansas Arkansas US 2020-04-05 23:06:45 34.29145 -91.37277 71 51013 Arlington Virginia US 2020-04-05 23:06:45 38.87677 -77.10140 72 42005 Armstrong Pennsylvania US 2020-04-05 23:06:45 40.81666 -79.46291 73 23003 Aroostook Maine US 2020-04-05 23:06:45 46.65926 -68.59841 74 22005 Ascension Louisiana US 2020-04-05 23:06:45 30.20406 -90.91328 75 37009 Ashe North Carolina US 2020-04-05 23:06:45 36.43296 -81.49863 76 39005 Ashland Ohio US 2020-04-05 23:06:45 40.84772 -82.27281 77 55003 Ashland Wisconsin US 2020-04-05 23:06:45 46.31957 -90.67837 78 5003 Ashley Arkansas US 2020-04-05 23:06:45 33.19153 -91.76985 79 39007 Ashtabula Ohio US 2020-04-05 23:06:45 41.70860 -80.74830 80 53003 Asotin Washington US 2020-04-05 23:06:45 46.18894 -117.20229 81 22007 Assumption Louisiana US 2020-04-05 23:06:45 29.89946 -91.06462 82 48013 Atascosa Texas US 2020-04-05 23:06:45 28.89333 -98.52730 83 20005 Atchison Kansas US 2020-04-05 23:06:45 39.53186 -95.30870 Confirmed Deaths Recovered Active Combined_Key 1 6 0 0 0 Abbeville, South Carolina, US 2 75 2 0 0 Acadia, Louisiana, US 3 11 0 0 0 Accomack, Virginia, US 4 385 3 0 0 Ada, Idaho, US 5 1 0 0 0 Adair, Iowa, US 6 3 0 0 0 Adair, Kentucky, US 7 10 0 0 0 Adair, Missouri, US 8 18 0 0 0 Adair, Oklahoma, US 9 354 10 0 0 Adams, Colorado, US 10 1 0 0 0 Adams, Idaho, US 11 3 0 0 0 Adams, Illinois, US 12 2 0 0 0 Adams, Indiana, US 13 19 0 0 0 Adams, Mississippi, US 14 11 0 0 0 Adams, Nebraska, US 15 2 0 0 0 Adams,Ohio,US 16 22 0 0 0 Adams, Pennsylvania, US 17 23 0 0 0 Adams, Washington, US 18 1 0 0 0 Adams, Wisconsin, US 19 40 0 0 0 Addison, Vermont, US 20 27 1 0 0 Aiken, South Carolina, US 21 123 0 0 0 Alachua, Florida, US 22 19 0 0 0 Alamance, North Carolina, US 23 566 12 0 0 Alameda, California, US 24 4 0 0 0 Alamosa, Colorado, US 25 305 8 0 0 Albany, New York, US 26 4 0 0 0 Albany, Wyoming, US 27 32 0 0 0 Albemarle, Virginia, US 28 6 0 0 0 Alcorn, Mississippi, US 29 2 0 0 0 Alexander, North Carolina, US 30 74 0 0 0 Alexandria, Virginia, US 31 17 1 0 0 Allamakee, Iowa, US 32 14 0 0 0 Allegan, Michigan, US 33 6 0 0 0 Allegany, Maryland, US 34 16 1 0 0 Allegany, New York, US 35 2 0 0 0 Alleghany, North Carolina, US 36 2 0 0 0 Alleghany, Virginia, US 37 605 4 0 0 Allegheny, Pennsylvania, US 38 63 2 0 0 Allen, Indiana, US 39 2 0 0 0 Allen, Kentucky, US 40 40 5 0 0 Allen, Louisiana, US 41 18 0 0 0 Allen, Ohio, US 42 2 0 0 0 Allendale, South Carolina, US 43 1 0 0 0 Alpine, California, US 44 0 0 0 0 Amador, California, US 45 6 0 0 0 Amelia, Virginia, US 46 6 0 0 0 Amherst, Virginia, US 47 6 1 0 0 Amite, Mississippi, US 48 85 1 0 0 Anchorage, Alaska, US 49 0 0 0 0 Anderson, Kansas, US 50 3 1 0 0 Anderson, Kentucky, US 51 69 3 0 0 Anderson, South Carolina, US 52 10 0 0 0 Anderson, Tennessee, US 53 1 0 0 0 Anderson, Texas, US 54 6 0 0 0 Andrews, Texas, US 55 20 0 0 0 Androscoggin, Maine, US 56 10 0 0 0 Angelina, Texas, US 57 319 6 0 0 Anne Arundel, Maryland, US 58 36 0 0 0 Anoka, Minnesota, US 59 5 0 0 0 Anson, North Carolina, US 60 1 0 0 0 Antelope, Nebraska, US 61 4 0 0 0 Antrim, Michigan, US 62 45 0 0 0 Apache,Arizona,US 63 1 1 0 0 Appanoose, Iowa, US 64 5 0 0 0 Appling, Georgia, US 65 0 0 0 0 Appomattox, Virginia, US 66 1 0 0 0 Aransas, Texas, US 67 608 13 0 0 Arapahoe, Colorado, US 68 1 0 0 0 Archuleta, Colorado, US 69 3 0 0 0 Arenac, Michigan, US 70 1 0 0 0 Arkansas, Arkansas, US 71 181 2 0 0 Arlington, Virginia, US 72 12 0 0 0 Armstrong, Pennsylvania, US 73 1 0 0 0 Aroostook, Maine, US 74 286 11 0 0 Ascension, Louisiana, US 75 1 0 0 0 Ashe, North Carolina, US 76 3 0 0 0 Ashland, Ohio, US 77 1 0 0 0 Ashland, Wisconsin, US 78 1 0 0 0 Ashley, Arkansas, US 79 12 0 0 0 Ashtabula, Ohio, US 80 2 0 0 0 Asotin, Washington, US 81 59 0 0 0 Assumption, Louisiana, US 82 2 0 0 0 Atascosa, Texas, US 83 2 0 0 0 Atchison, Kansas, US [ reached 'max' / getOption("max.print") -- omitted 2681 rows ] $time.series Province.State Country.Region Lat Long 2020-01-22 2020-01-23 1 Afghanistan 33.0000 65.0000 0 0 2 Albania 41.1533 20.1683 0 0 3 Algeria 28.0339 1.6596 0 0 4 Andorra 42.5063 1.5218 0 0 5 Angola -11.2027 17.8739 0 0 6 Antigua and Barbuda 17.0608 -61.7964 0 0 7 Argentina -38.4161 -63.6167 0 0 8 Armenia 40.0691 45.0382 0 0 9 Australian Capital Territory Australia -35.4735 149.0124 0 0 10 New South Wales Australia -33.8688 151.2093 0 0 11 Northern Territory Australia -12.4634 130.8456 0 0 12 Queensland Australia -28.0167 153.4000 0 0 2020-01-24 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 10 0 0 3 4 4 4 4 4 11 0 0 0 0 0 0 0 0 12 0 0 0 0 0 1 3 2 2020-02-01 2020-02-02 2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 10 4 4 4 4 4 4 4 4 11 0 0 0 0 0 0 0 0 12 3 2 2 3 3 4 5 5 2020-02-09 2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 10 4 4 4 4 4 4 4 4 11 0 0 0 0 0 0 0 0 12 5 5 5 5 5 5 5 5 2020-02-17 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 1 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 10 4 4 4 4 4 4 4 4 11 0 0 0 0 0 0 0 0 12 5 5 5 5 5 5 5 5 2020-02-25 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03 1 1 1 1 1 1 1 1 1 2 0 0 0 0 0 0 0 0 3 1 1 1 1 1 1 3 5 4 0 0 0 0 0 0 1 1 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 1 8 0 0 0 0 0 1 1 1 9 0 0 0 0 0 0 0 0 10 4 4 4 4 4 6 6 13 11 0 0 0 0 0 0 0 0 12 5 5 5 5 9 9 9 11 2020-03-04 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11 1 1 1 1 1 4 4 5 7 2 0 0 0 0 0 2 10 12 3 12 12 17 17 19 20 20 20 4 1 1 1 1 1 1 1 1 5 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 7 1 1 2 8 12 12 17 19 8 1 1 1 1 1 1 1 1 9 0 0 0 0 0 0 0 0 10 22 22 26 28 38 48 55 65 11 1 1 0 0 0 0 1 1 12 11 13 13 13 15 15 18 20 2020-03-12 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18 2020-03-19 1 7 7 11 16 21 22 22 22 2 23 33 38 42 51 55 59 64 3 24 26 37 48 54 60 74 87 4 1 1 1 1 2 39 39 53 5 0 0 0 0 0 0 0 0 6 0 1 1 1 1 1 1 1 7 19 31 34 45 56 68 79 97 8 4 8 18 26 52 78 84 115 9 0 1 1 1 2 2 3 4 10 65 92 112 134 171 210 267 307 11 1 1 1 1 1 1 1 1 12 20 35 46 61 68 78 94 144 2020-03-20 2020-03-21 2020-03-22 2020-03-23 2020-03-24 2020-03-25 2020-03-26 2020-03-27 1 24 24 40 40 74 84 94 110 2 70 76 89 104 123 146 174 186 3 90 139 201 230 264 302 367 409 4 75 88 113 133 164 188 224 267 5 1 2 2 3 3 3 4 4 6 1 1 1 3 3 3 7 7 7 128 158 266 301 387 387 502 589 8 136 160 194 235 249 265 290 329 9 6 9 19 32 39 39 53 62 10 353 436 669 669 818 1029 1219 1405 11 3 3 5 5 6 6 12 12 12 184 221 259 319 397 443 493 555 2020-03-28 2020-03-29 2020-03-30 2020-03-31 2020-04-01 2020-04-02 2020-04-03 2020-04-04 1 110 120 170 174 237 273 281 299 2 197 212 223 243 259 277 304 333 3 454 511 584 716 847 986 1171 1251 4 308 334 370 376 390 428 439 466 5 5 7 7 7 8 8 8 10 6 7 7 7 7 7 9 15 15 7 690 745 820 1054 1054 1133 1265 1451 8 407 424 482 532 571 663 736 770 9 71 77 78 80 84 87 91 93 10 1617 1791 2032 2032 2182 2298 2389 2493 11 15 15 15 17 19 21 22 26 12 625 656 689 743 781 835 873 900 2020-04-05 status 1 349 confirmed 2 361 confirmed 3 1320 confirmed 4 501 confirmed 5 14 confirmed 6 15 confirmed 7 1451 confirmed 8 822 confirmed 9 96 confirmed 10 2580 confirmed 11 27 confirmed 12 907 confirmed [ reached 'max' / getOption("max.print") -- omitted 760 rows ] $ts.dep Province.State Country.Region Lat Long 2020-01-22 2020-01-23 2020-01-24 1 Thailand 15.0000 101.0000 2 3 5 2 Japan 36.0000 138.0000 2 1 2 3 Singapore 1.2833 103.8333 0 1 3 4 Nepal 28.1667 84.2500 0 0 0 5 Malaysia 2.5000 112.5000 0 0 0 6 British Columbia Canada 49.2827 -123.1207 0 0 0 7 New South Wales Australia -33.8688 151.2093 0 0 0 8 Victoria Australia -37.8136 144.9631 0 0 0 9 Queensland Australia -28.0167 153.4000 0 0 0 10 Cambodia 11.5500 104.9167 0 0 0 11 Sri Lanka 7.0000 81.0000 0 0 0 12 Germany 51.0000 9.0000 0 0 0 13 Finland 64.0000 26.0000 0 0 0 14 United Arab Emirates 24.0000 54.0000 0 0 0 2020-01-25 2020-01-26 2020-01-27 2020-01-28 2020-01-29 2020-01-30 2020-01-31 2020-02-01 1 7 8 8 14 14 14 19 19 2 2 4 4 7 7 11 15 20 3 3 4 5 7 7 10 13 16 4 1 1 1 1 1 1 1 1 5 3 4 4 4 7 8 8 8 6 0 0 0 1 1 1 1 1 7 0 3 4 4 4 4 4 4 8 0 1 1 1 1 2 3 4 9 0 0 0 0 1 3 2 3 10 0 0 1 1 1 1 1 1 11 0 0 1 1 1 1 1 1 12 0 0 1 4 4 4 5 8 13 0 0 0 0 1 1 1 1 14 0 0 0 0 4 4 4 4 2020-02-02 2020-02-03 2020-02-04 2020-02-05 2020-02-06 2020-02-07 2020-02-08 2020-02-09 1 19 19 25 25 25 25 32 32 2 20 20 22 22 45 25 25 26 3 18 18 24 28 28 30 33 40 4 1 1 1 1 1 1 1 1 5 8 8 10 12 12 12 16 16 6 1 1 1 2 2 4 4 4 7 4 4 4 4 4 4 4 4 8 4 4 4 4 4 4 4 4 9 2 2 3 3 4 5 5 5 10 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 12 10 12 12 12 12 13 13 14 13 1 1 1 1 1 1 1 1 14 5 5 5 5 5 5 7 7 2020-02-10 2020-02-11 2020-02-12 2020-02-13 2020-02-14 2020-02-15 2020-02-16 2020-02-17 1 32 33 33 33 33 33 34 35 2 26 26 28 28 29 43 59 66 3 45 47 50 58 67 72 75 77 4 1 1 1 1 1 1 1 1 5 18 18 18 19 19 22 22 22 6 4 4 4 4 4 4 4 5 7 4 4 4 4 4 4 4 4 8 4 4 4 4 4 4 4 4 9 5 5 5 5 5 5 5 5 10 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 12 14 16 16 16 16 16 16 16 13 1 1 1 1 1 1 1 1 14 8 8 8 8 8 8 9 9 2020-02-18 2020-02-19 2020-02-20 2020-02-21 2020-02-22 2020-02-23 2020-02-24 2020-02-25 1 35 35 35 35 35 35 35 37 2 74 84 94 105 122 147 159 170 3 81 84 84 85 85 89 89 91 4 1 1 1 1 1 1 1 1 5 22 22 22 22 22 22 22 22 6 5 5 5 6 6 6 6 7 7 4 4 4 4 4 4 4 4 8 4 4 4 4 4 4 4 4 9 5 5 5 5 5 5 5 5 10 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 12 16 16 16 16 16 16 16 17 13 1 1 1 1 1 1 1 1 14 9 9 9 9 13 13 13 13 2020-02-26 2020-02-27 2020-02-28 2020-02-29 2020-03-01 2020-03-02 2020-03-03 2020-03-04 1 40 40 41 42 42 43 43 43 2 189 214 228 241 256 274 293 331 3 93 93 93 102 106 108 110 110 4 1 1 1 1 1 1 1 1 5 22 23 23 25 29 29 36 50 6 7 7 7 8 8 8 9 12 7 4 4 4 4 6 6 13 22 8 4 4 4 7 7 9 9 10 9 5 5 5 9 9 9 11 11 10 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 12 27 46 48 79 130 159 196 262 13 2 2 2 3 6 6 6 6 14 13 13 19 21 21 21 27 27 2020-03-05 2020-03-06 2020-03-07 2020-03-08 2020-03-09 2020-03-10 2020-03-11 2020-03-12 1 47 48 50 50 50 53 59 70 2 360 420 461 502 511 581 639 639 3 117 130 138 150 150 160 178 178 4 1 1 1 1 1 1 1 1 5 50 83 93 99 117 129 149 149 6 13 21 21 27 32 32 39 46 7 22 26 28 38 48 55 65 65 8 10 10 11 11 15 18 21 21 9 13 13 13 15 15 18 20 20 10 1 1 1 2 2 2 3 3 11 1 1 1 1 1 1 2 2 12 482 670 799 1040 1176 1457 1908 2078 13 12 15 15 23 30 40 59 59 14 29 29 45 45 45 74 74 85 2020-03-13 2020-03-14 2020-03-15 2020-03-16 2020-03-17 2020-03-18 2020-03-19 2020-03-20 1 75 82 114 147 177 212 272 322 2 701 773 839 825 878 889 924 963 3 200 212 226 243 266 313 345 385 4 1 1 1 1 1 1 1 1 5 197 238 428 566 673 790 900 1030 6 64 64 73 103 103 186 231 271 7 92 112 134 171 210 267 307 353 8 36 49 57 71 94 121 121 121 9 35 46 61 68 78 94 144 184 10 5 7 7 7 33 35 37 51 11 6 10 18 28 44 51 60 73 12 3675 4585 5795 7272 9257 12327 15320 19848 13 155 225 244 277 321 336 400 450 14 85 85 98 98 98 113 140 140 2020-03-21 2020-03-22 2020-03-23 status 1 411 599 599 confirmed 2 1007 1086 1086 confirmed 3 432 455 455 confirmed 4 1 2 2 confirmed 5 1183 1306 1306 confirmed 6 424 424 424 confirmed 7 436 533 533 confirmed 8 229 296 296 confirmed 9 221 221 221 confirmed 10 53 84 84 confirmed 11 77 82 82 confirmed 12 22213 24873 24873 confirmed 13 523 626 626 confirmed 14 153 153 153 confirmed [ reached 'max' / getOption("max.print") -- omitted 1489 rows ]