library(AER) library(MASS) coefs <- summary(lm(medv ~ lstat + crim + age, data = Boston))$coef[, 1] SEs <- summary(lm(medv ~ lstat + crim + age, data = Boston))$coef[, 2] # compute t-statistics for all coefficients. Assign them to `tstats` # compute p-values for all significance tests. Assign them to `pval` # check whether the hypotheses are rejected at the 1% significance level # compute t-statistics for each coefficient. Assign them to `tstat` tstats <- coefs/SEs # compute p-values for all significance tests. Assign them to `pval` pvals <- 2*(pnorm(-abs(tstats))) # check whether the hypotheses are rejected at the 1% significance level pvals < 0.01 test_object("tstats") test_object("pvals") test_or({test_output_contains("pvals < 0.01"), },{ test_output_contains("pvals =< 0.01") },{ test_output_contains("pvals >= 0.01") },{} test_output_contains("pvals > 0.01")}) success_msg("All coefficients but the one on the crime rate (crim) are statistically different from zero at the 1% significance level.")