# The evals data frame is already loaded into the workspace # The full model: m_full <- lm(score ~ rank + ethnicity + gender + language + age + cls_perc_eval + cls_students + cls_level + cls_profs + cls_credits + bty_avg, data = evals) summary(m_full)$adj.r.squared # Remove rank: m1 <- lm(score ~ ethnicity + gender + language + age + cls_perc_eval + cls_students + cls_level + cls_profs + cls_credits + bty_avg, data = evals) summary(m1)$adj.r.squared # Remove ethnicity: m2 <- lm(score ~ rank + gender + language + age + cls_perc_eval + cls_students + cls_level + cls_profs + cls_credits + bty_avg, data = evals) summary(m2)$adj.r.squared # Remove gender: m3 <- lm(score ~ rank + ethnicity + language + age + cls_perc_eval + cls_students + cls_level + cls_profs + cls_credits + bty_avg, data = evals) summary(m3)$adj.r.squared # Remove language: m4 <- lm(score ~ rank + ethnicity + gender + age + cls_perc_eval + cls_students + cls_level + cls_profs + cls_credits + bty_avg, data = evals) summary(m4)$adj.r.squared # Remove age: m5 <- lm(score ~ rank + ethnicity + gender + language + cls_perc_eval + cls_students + cls_level + cls_profs + cls_credits + bty_avg, data = evals) summary(m5)$adj.r.squared # Remove cls_perc_eval: m6 <- lm(score ~ rank + ethnicity + gender + language + age + cls_students + cls_level + cls_profs + cls_credits + bty_avg, data = evals) summary(m6)$adj.r.squared # Remove cls_students: m7 <- lm(score ~ rank + ethnicity + gender + language + age + cls_perc_eval + cls_level + cls_profs + cls_credits + bty_avg, data = evals) summary(m7)$adj.r.squared # Remove cls_level: m8 <- lm(score ~ rank + ethnicity + gender + language + age + cls_perc_eval + cls_students + cls_profs + cls_credits + bty_avg, data = evals) summary(m8)$adj.r.squared # Remove cls_profs: m9 <- lm(score ~ rank + ethnicity + gender + language + age + cls_perc_eval + cls_students + cls_level + cls_credits + bty_avg, data = evals) summary(m9)$adj.r.squared # Remove cls_credits: m10 <- lm(score ~ rank + ethnicity + gender + language + age + cls_perc_eval + cls_students + cls_level + cls_profs + bty_avg, data = evals) summary(m10)$adj.r.squared # Remove bty_avg: m11 <- lm(score ~ rank + ethnicity + gender + language + age + cls_perc_eval + cls_students + cls_level + cls_profs + cls_credits, data = evals) summary(m11)$adj.r.squared

Question 6

Data Analysis and Statistical Inference

Exercise instructions

Hands-on interactive exercise