Fast Campus: Data Analytics 16일차 (4/19) Part 2

금일 수업 내용입니다.

단순 Text 파일이므로 그냥 Linux상에 복사& 붙여넣기만으로 실행됩니다. 

 

# The 'evals' data frame is already loaded into the workspace

# R^2 값이 가장 커질수 있도록 설명 변수를 하나씩 제거해 본다 

# 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

Lecture 16