plot(collgpa ~ hsgpa, data = Gpa)
mod <- lm(collgpa ~ hsgpa, data = Gpa)
abline(mod) # add line
yhat <- predict(mod) # fitted values
e <- resid(mod) # residuals
cbind(Gpa, yhat, e) # Table 2.1
cor(Gpa$hsgpa, Gpa$collgpa)
if (FALSE) {
library(ggplot2)
ggplot2::ggplot(data = Gpa, aes(x = hsgpa, y = collgpa)) +
geom_point() +
geom_smooth(method = "lm") +
theme_bw()
}
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