Page 300 - Applied Statistics with R

300 CHAPTER 13. MODEL DIAGNOSTICS

big_mod_cd = cooks.distance(big_model)
sum(big_mod_cd > 4 / length(big_mod_cd))

## [1] 31

Here, we find 31, so perhaps removing them will help!

big_model_fix = lm(mpg ~ disp * hp * domestic,
data = autompg,
subset = big_mod_cd < 4 / length(big_mod_cd))
qqnorm(resid(big_model_fix), col = "grey")
qqline(resid(big_model_fix), col = "dodgerblue", lwd = 2)

Normal Q-Q Plot

10

Sample Quantiles 5 0

-5

-3 -2 -1 0 1 2 3

Theoretical Quantiles

shapiro.test(resid(big_model_fix))

##
## Shapiro-Wilk normality test
##
## data: resid(big_model_fix)
## W = 0.99035, p-value = 0.02068

Removing these points results in a much better Q-Q plot, and now Shapiro-Wilk
fails to reject for a low .