1. Create a table with all three models, including the robust standard errors.

How would that look like with these

Model 1

Call: Im(formula = DV ~ IV, data = terrorism) Residuals : Min 10 Median 30 Max -0. 6450 -0.6450 -0.2529 -0.2520 17.3448 Coefficients: Estimate Std. Error t value Pr(alt1) (Intercept) 0.2529 0.2626 0.963 0.337 IV 0.3921 0.3365 1.165 0.245 Residual standard error: 2.197 on 177 degrees of freedom (5 observations deleted due to missingness) Multiple R-squared: 0.097613, Adjusted R-squared: 0.002006 F-statistic: 1.358 on 1 and 177 DF, p-value: 0.2455Residuals: Min 10 Median 30 Max -0.3506 -0.3327 -0.0721 -0.0114 3.5105 Coefficients: Estimate Std. Error t value Pr(>Itl) (Intercept) 0. 073766 0. 099038 0.745 0.4575 IV 0.277099 0. 112200 2.470 0. 0146 gappc -0 . 002063 0.054936 -0.418 0.6766 Signif. codes: 0 0. 091 "' 0.01 0.05 '. * 0.1 ' ' Residual standard error: 0.5982 on 151 degrees of freedom (30 observations deleted due to missingness) Multiple R-squared: 0.05971, Adjusted R-squared: 0.04725 F-statistic: 4.794 on 2 and 151 DF, p-value: 0.009581m(formula = DV ~ IV + gdppc + ethnic + religion + mideast, data = terrorism) tesiduals: Min 10 Median 30 Max 0. 6906 -0.2781 -0.0911 -0.0060 3.4836 Coefficients: Estimate Std. Error t value Pr(>Itl) "Intercept) 0.0758903 0.1623722 0. 467 0. 6409 V 0. 2150130 0.1189963 1. 807 0.0729 dppc -0. 0009779 0. 0952788 -0.185 0.8533 thnic 0. 1839491 0. 2220878 0. 828 0. 4089 eligion -0.1735284 0.2231728 -0. 778 0.4381 mideast 0. 3138214 0.2313647 1. 356 0.1771 ignif. codes: 0 ' * *' 0. 001 (*' 0.01 ' 0.95 '.' 0.1 . ' 1 tesidual standard error: 0.6909 on 144 degrees of freedom (34 observations deleted due to missingness) Multiple R-squared: 0.07662, Adjusted R-squared: 0.04456 -statistic: 2.39 on 5 and 144 DF, p-value: 0.04074