setwd("~/Dropbox/Research/Adrian")
a<-read.csv ("AI_vs_human_conservatism_risk.csv", header=T, sep=",")
summary(lm(AIriskCar_1 ~ poli_2, a))
##
## Call:
## lm(formula = AIriskCar_1 ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -62.554 -21.713 6.397 23.178 37.446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64.99393 5.22922 12.429 <2e-16 ***
## poli_2 -0.02440 0.07545 -0.323 0.747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27.63 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0006788, Adjusted R-squared: -0.00581
## F-statistic: 0.1046 on 1 and 154 DF, p-value: 0.7468
summary(lm(AIriskCar_1 ~ poli_1 + age + gender + edu, a))
##
## Call:
## lm(formula = AIriskCar_1 ~ poli_1 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -66.274 -19.882 3.374 21.352 41.520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.34024 11.38875 4.069 7.59e-05 ***
## poli_1 -0.01887 0.07358 -0.256 0.79793
## age 0.51123 0.16566 3.086 0.00241 **
## gender 9.11862 4.26095 2.140 0.03396 *
## edu -3.29305 1.68553 -1.954 0.05258 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.48 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1, Adjusted R-squared: 0.0762
## F-statistic: 4.196 on 4 and 151 DF, p-value: 0.002993
summary(lm(AIriskCar_1 ~ poli_2 + age + gender + edu, a))
##
## Call:
## lm(formula = AIriskCar_1 ~ poli_2 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -68.624 -18.648 3.352 21.592 42.059
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.23763 11.29417 3.828 0.000189 ***
## poli_2 0.04756 0.07647 0.622 0.534913
## age 0.52862 0.16612 3.182 0.001775 **
## gender 9.32350 4.24763 2.195 0.029691 *
## edu -3.73324 1.73290 -2.154 0.032800 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.46 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1019, Adjusted R-squared: 0.07816
## F-statistic: 4.285 on 4 and 151 DF, p-value: 0.002592
summary(lm(AIriskCar_1 ~ poli_3 + age + gender + edu, a))
##
## Call:
## lm(formula = AIriskCar_1 ~ poli_3 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.810 -21.112 4.005 20.966 41.834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.21975 11.16146 4.231 4.03e-05 ***
## poli_3 -0.04316 0.07040 -0.613 0.54080
## age 0.51450 0.16474 3.123 0.00215 **
## gender 8.92726 4.26747 2.092 0.03812 *
## edu -3.15514 1.68568 -1.872 0.06318 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.46 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1019, Adjusted R-squared: 0.07809
## F-statistic: 4.282 on 4 and 151 DF, p-value: 0.002605
summary(lm(AIcomfCar_1 ~ poli_2, a))
##
## Call:
## lm(formula = AIcomfCar_1 ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.872 -30.879 -3.489 28.309 69.096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.90446 6.22039 4.968 1.78e-06 ***
## poli_2 0.19966 0.08976 2.225 0.0276 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.87 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03113, Adjusted R-squared: 0.02484
## F-statistic: 4.949 on 1 and 154 DF, p-value: 0.02757
summary(lm(AIcomfCar_1 ~ poli_1 + age + gender + edu, a))
##
## Call:
## lm(formula = AIcomfCar_1 ~ poli_1 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.471 -25.056 -3.213 26.665 66.502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.63467 13.56738 3.290 0.00125 **
## poli_1 0.13377 0.08766 1.526 0.12911
## age -0.35811 0.19735 -1.815 0.07157 .
## gender -13.31799 5.07605 -2.624 0.00959 **
## edu 5.56690 2.00797 2.772 0.00627 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.55 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1249, Adjusted R-squared: 0.1017
## F-statistic: 5.388 on 4 and 151 DF, p-value: 0.0004405
summary(lm(AIcomfCar_1 ~ poli_2 + age + gender + edu, a))
##
## Call:
## lm(formula = AIcomfCar_1 ~ poli_2 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.504 -25.894 -3.424 26.003 65.128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.51408 13.51016 3.443 0.000745 ***
## poli_2 0.10807 0.09148 1.181 0.239297
## age -0.35648 0.19872 -1.794 0.074831 .
## gender -13.65499 5.08104 -2.687 0.008008 **
## edu 5.46233 2.07290 2.635 0.009288 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.65 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1195, Adjusted R-squared: 0.09621
## F-statistic: 5.125 on 4 and 151 DF, p-value: 0.0006712
summary(lm(AIcomfCar_1 ~ poli_3 + age + gender + edu, a))
##
## Call:
## lm(formula = AIcomfCar_1 ~ poli_3 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54.580 -24.441 -3.642 27.241 67.951
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.85606 13.21559 3.319 0.00113 **
## poli_3 0.17748 0.08336 2.129 0.03487 *
## age -0.38360 0.19506 -1.967 0.05106 .
## gender -12.78613 5.05285 -2.530 0.01241 *
## edu 5.28447 1.99591 2.648 0.00897 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.33 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1373, Adjusted R-squared: 0.1144
## F-statistic: 6.008 on 4 and 151 DF, p-value: 0.0001634
summary(lm(AIriskM_1 ~ poli_2, a))
##
## Call:
## lm(formula = AIriskM_1 ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -63.571 -19.360 6.613 19.508 42.569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66.64113 4.86337 13.70 <2e-16 ***
## poli_2 -0.12281 0.07017 -1.75 0.0821 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.7 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0195, Adjusted R-squared: 0.01313
## F-statistic: 3.063 on 1 and 154 DF, p-value: 0.08211
summary(lm(AIriskM_1 ~ poli_1 + age + gender + edu, a))
##
## Call:
## lm(formula = AIriskM_1 ~ poli_1 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -68.190 -19.030 4.445 19.119 47.790
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.08105 10.86756 4.424 1.84e-05 ***
## poli_1 -0.13491 0.07022 -1.921 0.0566 .
## age 0.27237 0.15808 1.723 0.0869 .
## gender 7.36918 4.06595 1.812 0.0719 .
## edu -0.57565 1.60840 -0.358 0.7209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.27 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.07044, Adjusted R-squared: 0.04581
## F-statistic: 2.861 on 4 and 151 DF, p-value: 0.02544
summary(lm(AIriskM_1 ~ poli_2 + age + gender + edu, a))
##
## Call:
## lm(formula = AIriskM_1 ~ poli_2 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -63.935 -18.662 4.464 19.506 46.180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.92931 10.84900 4.234 3.98e-05 ***
## poli_2 -0.10332 0.07346 -1.407 0.1616
## age 0.27232 0.15957 1.707 0.0900 .
## gender 7.72327 4.08020 1.893 0.0603 .
## edu -0.51127 1.66459 -0.307 0.7592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.41 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.06003, Adjusted R-squared: 0.03513
## F-statistic: 2.411 on 4 and 151 DF, p-value: 0.05165
summary(lm(AIriskM_1 ~ poli_3 + age + gender + edu, a))
##
## Call:
## lm(formula = AIriskM_1 ~ poli_3 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -68.93 -19.11 3.25 18.90 46.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.58605 10.61886 4.481 1.46e-05 ***
## poli_3 -0.14886 0.06698 -2.222 0.0277 *
## age 0.29863 0.15673 1.905 0.0586 .
## gender 7.02628 4.06001 1.731 0.0856 .
## edu -0.45391 1.60373 -0.283 0.7775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.17 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.07788, Adjusted R-squared: 0.05345
## F-statistic: 3.188 on 4 and 151 DF, p-value: 0.01511
a$cons<-(a$poli_1+a$poli_2+a$poli_3)
summary(lm(AIriskM_1 ~ cons + age + gender + edu, a))
##
## Call:
## lm(formula = AIriskM_1 ~ cons + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -67.630 -19.430 3.728 19.072 46.707
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.28265 10.83581 4.456 1.62e-05 ***
## cons -0.05080 0.02523 -2.014 0.0458 *
## age 0.27524 0.15771 1.745 0.0830 .
## gender 7.29787 4.06289 1.796 0.0745 .
## edu -0.35918 1.62923 -0.220 0.8258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.24 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.07262, Adjusted R-squared: 0.04805
## F-statistic: 2.956 on 4 and 151 DF, p-value: 0.02186
summary(lm(AIcomfM_1 ~ poli_2, a))
##
## Call:
## lm(formula = AIcomfM_1 ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -56.369 -26.449 7.219 21.066 62.282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.71767 5.59329 6.743 2.93e-10 ***
## poli_2 0.18651 0.08071 2.311 0.0222 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.56 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03352, Adjusted R-squared: 0.02724
## F-statistic: 5.34 on 1 and 154 DF, p-value: 0.02216
summary(lm(AIcomfM_1 ~ poli_1 + age + gender + edu, a))
##
## Call:
## lm(formula = AIcomfM_1 ~ poli_1 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -58.993 -26.702 4.905 21.824 57.799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.37828 12.57051 4.246 3.78e-05 ***
## poli_1 0.15750 0.08122 1.939 0.0543 .
## age -0.21336 0.18285 -1.167 0.2451
## gender -9.41736 4.70309 -2.002 0.0470 *
## edu 2.06055 1.86043 1.108 0.2698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.23 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.07316, Adjusted R-squared: 0.04861
## F-statistic: 2.98 on 4 and 151 DF, p-value: 0.02106
summary(lm(AIcomfM_1 ~ poli_2 + age + gender + edu, a))
##
## Call:
## lm(formula = AIcomfM_1 ~ poli_2 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -58.344 -26.918 5.758 23.247 56.849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.49653 12.50193 4.359 2.4e-05 ***
## poli_2 0.15148 0.08465 1.790 0.0755 .
## age -0.20464 0.18389 -1.113 0.2675
## gender -9.75347 4.70185 -2.074 0.0397 *
## edu 1.76189 1.91821 0.919 0.3598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.28 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0698, Adjusted R-squared: 0.04516
## F-statistic: 2.833 on 4 and 151 DF, p-value: 0.02659
summary(lm(AIcomfM_1 ~ poli_3 + age + gender + edu, a))
##
## Call:
## lm(formula = AIcomfM_1 ~ poli_3 + age + gender + edu, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -60.415 -25.551 4.014 20.893 59.386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.29359 12.24346 4.353 2.47e-05 ***
## poli_3 0.18939 0.07723 2.452 0.0153 *
## age -0.24373 0.18071 -1.349 0.1794
## gender -8.91688 4.68116 -1.905 0.0587 .
## edu 1.83400 1.84909 0.992 0.3229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.02 on 151 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.08646, Adjusted R-squared: 0.06226
## F-statistic: 3.573 on 4 and 151 DF, p-value: 0.008159
#risk/comfort with humans
summary(lm(hriskCar_1 ~ poli_2, a))
##
## Call:
## lm(formula = hriskCar_1 ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.235 -19.512 1.552 18.955 52.924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.08448 4.68833 9.830 <2e-16 ***
## poli_2 0.03540 0.06765 0.523 0.601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.78 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.001775, Adjusted R-squared: -0.004707
## F-statistic: 0.2739 on 1 and 154 DF, p-value: 0.6015
summary(lm(hcomfCar_1 ~ poli_2, a))
##
## Call:
## lm(formula = hcomfCar_1 ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.166 -13.878 5.222 17.927 35.735
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 65.51710 4.63969 14.121 <2e-16 ***
## poli_2 -0.01252 0.06695 -0.187 0.852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.52 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0002271, Adjusted R-squared: -0.006265
## F-statistic: 0.03499 on 1 and 154 DF, p-value: 0.8519
summary(lm(hriskM_1 ~ poli_2, a))
##
## Call:
## lm(formula = hriskM_1 ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -45.617 -18.963 -1.809 19.012 58.785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.61691 4.91498 9.281 <2e-16 ***
## poli_2 -0.04402 0.07092 -0.621 0.536
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.97 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.002496, Adjusted R-squared: -0.003982
## F-statistic: 0.3853 on 1 and 154 DF, p-value: 0.5357
summary(lm(hcomfCar_1 ~ poli_2, a))
##
## Call:
## lm(formula = hcomfCar_1 ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.166 -13.878 5.222 17.927 35.735
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 65.51710 4.63969 14.121 <2e-16 ***
## poli_2 -0.01252 0.06695 -0.187 0.852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.52 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0002271, Adjusted R-squared: -0.006265
## F-statistic: 0.03499 on 1 and 154 DF, p-value: 0.8519
#mediation with all 3 conservatism measures (1=general, 2=social, 3=fiscal)
library("mediation")
## Loading required package: MASS
## Loading required package: Matrix
## Loading required package: mvtnorm
## Loading required package: sandwich
## mediation: Causal Mediation Analysis
## Version: 4.4.6
med.fit<-lm(AIriskM_1 ~ poli_1, a)
out.fit<-lm(AIcomfM_1 ~ poli_1 + AIriskM_1, a)
med.out <- mediate(med.fit, out.fit, treat = "poli_1", mediator = "AIriskM_1",boot=TRUE, sims = 1000)
summary(med.out)
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.11950 0.00393 0.23 0.040 *
## ADE 0.07274 -0.06443 0.21 0.246
## Total Effect 0.19223 0.01904 0.37 0.030 *
## Prop. Mediated 0.62162 0.01228 1.77 0.046 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 156
##
##
## Simulations: 1000
med.fit<-lm(AIriskM_1 ~ poli_2, a)
out.fit<-lm(AIcomfM_1 ~ poli_2 + AIriskM_1, a)
med.out <- mediate(med.fit, out.fit, treat = "poli_2", mediator = "AIriskM_1",boot=TRUE, sims = 1000)
summary(med.out)
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.0944 -0.0295 0.21 0.118
## ADE 0.0921 -0.0204 0.21 0.124
## Total Effect 0.1865 0.0118 0.36 0.036 *
## Prop. Mediated 0.5062 -0.4562 1.41 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 156
##
##
## Simulations: 1000
med.fit<-lm(AIriskM_1 ~ poli_3, a)
out.fit<-lm(AIcomfM_1 ~ poli_3 + AIriskM_1, a)
med.out <- mediate(med.fit, out.fit, treat = "poli_3", mediator = "AIriskM_1",boot=TRUE, sims = 1000)
summary(med.out)
##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 0.1219 0.0119 0.22 0.034 *
## ADE 0.0950 -0.0265 0.22 0.120
## Total Effect 0.2169 0.0489 0.38 0.008 **
## Prop. Mediated 0.5619 0.1099 1.24 0.034 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 156
##
##
## Simulations: 1000
#difference scores (AI - human)
a$carriskdiff<-a$AIriskCar_1-a$hriskCar_1
a$carcomfdiff<-a$AIcomfCar_1-a$hcomfCar_1
a$medriskdiff<-a$AIriskM_1-a$hriskM_1
a$medcomfdiff<-a$AIcomfM_1-a$hcomfMed_1
#poli 1 is general conservatism
summary(lm(carriskdiff ~ poli_1, a))
##
## Call:
## lm(formula = carriskdiff ~ poli_1, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -102.37 -21.44 -4.60 28.10 78.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.38192 6.25907 3.256 0.00139 **
## poli_1 -0.09015 0.09609 -0.938 0.34960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 35.6 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.005683, Adjusted R-squared: -0.0007731
## F-statistic: 0.8803 on 1 and 154 DF, p-value: 0.3496
summary(lm(carcomfdiff ~ poli_1, a))
##
## Call:
## lm(formula = carcomfdiff ~ poli_1, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.053 -32.193 6.225 25.122 108.965
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -32.7795 6.8585 -4.779 4.07e-06 ***
## poli_1 0.1981 0.1053 1.882 0.0617 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39.01 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02248, Adjusted R-squared: 0.01613
## F-statistic: 3.542 on 1 and 154 DF, p-value: 0.06173
summary(lm(medriskdiff ~ poli_1, a))
##
## Call:
## lm(formula = medriskdiff ~ poli_1, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -97.66 -20.40 -2.00 24.78 78.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.73969 5.91345 3.676 0.000326 ***
## poli_1 -0.09764 0.09078 -1.076 0.283777
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 33.64 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.007457, Adjusted R-squared: 0.001012
## F-statistic: 1.157 on 1 and 154 DF, p-value: 0.2838
summary(lm(medcomfdiff ~ poli_1, a))
##
## Call:
## lm(formula = medcomfdiff ~ poli_1, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -67.53 -26.72 6.84 24.04 79.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -34.59487 5.75056 -6.016 1.25e-08 ***
## poli_1 0.19242 0.08828 2.180 0.0308 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.71 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02993, Adjusted R-squared: 0.02363
## F-statistic: 4.751 on 1 and 154 DF, p-value: 0.0308
#poli 2 is social conservatism
summary(lm(carriskdiff ~ poli_2, a))
##
## Call:
## lm(formula = carriskdiff ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -104.587 -20.586 -4.944 27.082 80.413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.90946 6.74805 2.802 0.00573 **
## poli_2 -0.05981 0.09737 -0.614 0.53997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 35.66 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.002444, Adjusted R-squared: -0.004034
## F-statistic: 0.3773 on 1 and 154 DF, p-value: 0.54
summary(lm(carcomfdiff ~ poli_2, a))
##
## Call:
## lm(formula = carcomfdiff ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -79.272 -31.488 5.351 24.315 111.728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -34.6126 7.3720 -4.695 5.85e-06 ***
## poli_2 0.2122 0.1064 1.995 0.0478 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38.96 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02519, Adjusted R-squared: 0.01886
## F-statistic: 3.979 on 1 and 154 DF, p-value: 0.04784
summary(lm(medriskdiff ~ poli_2, a))
##
## Call:
## lm(formula = medriskdiff ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -97.927 -20.029 -3.052 24.889 78.976
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.02421 6.37376 3.299 0.00121 **
## poli_2 -0.07878 0.09197 -0.857 0.39297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 33.68 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.004743, Adjusted R-squared: -0.00172
## F-statistic: 0.7339 on 1 and 154 DF, p-value: 0.393
summary(lm(medcomfdiff ~ poli_2, a))
##
## Call:
## lm(formula = medcomfdiff ~ poli_2, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -68.560 -27.325 7.717 23.691 78.959
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -32.0922 6.2371 -5.145 8.02e-07 ***
## poli_2 0.1379 0.0900 1.532 0.128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.96 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01501, Adjusted R-squared: 0.00861
## F-statistic: 2.346 on 1 and 154 DF, p-value: 0.1276
#poli 3 is fiscal conservatism
summary(lm(carriskdiff ~ poli_3, a))
##
## Call:
## lm(formula = carriskdiff ~ poli_3, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -97.211 -21.334 -2.906 29.983 72.398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.46200 5.79205 4.569 1e-05 ***
## poli_3 -0.20251 0.09067 -2.234 0.027 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 35.14 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03138, Adjusted R-squared: 0.02509
## F-statistic: 4.989 on 1 and 154 DF, p-value: 0.02695
summary(lm(carcomfdiff ~ poli_3, a))
##
## Call:
## lm(formula = carcomfdiff ~ poli_3, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -77.482 -32.226 4.646 25.612 105.950
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -35.62556 6.36817 -5.594 9.87e-08 ***
## poli_3 0.25675 0.09969 2.576 0.0109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38.63 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0413, Adjusted R-squared: 0.03507
## F-statistic: 6.634 on 1 and 154 DF, p-value: 0.01095
summary(lm(medriskdiff ~ poli_3, a))
##
## Call:
## lm(formula = medriskdiff ~ poli_3, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -97.967 -19.457 -1.084 22.931 73.656
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.02376 5.43561 5.340 3.29e-07 ***
## poli_3 -0.23186 0.08509 -2.725 0.00718 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.98 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.046, Adjusted R-squared: 0.0398
## F-statistic: 7.425 on 1 and 154 DF, p-value: 0.007177
summary(lm(medcomfdiff ~ poli_3, a))
##
## Call:
## lm(formula = medcomfdiff ~ poli_3, data = a)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.135 -27.556 6.801 23.691 74.883
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -37.44559 5.31959 -7.039 5.99e-11 ***
## poli_3 0.25089 0.08327 3.013 0.00303 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.27 on 154 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.05566, Adjusted R-squared: 0.04953
## F-statistic: 9.078 on 1 and 154 DF, p-value: 0.003026