ings)
fm &lt;- lm(eval ~ beauty, data = TeachingRatings)
abline(fm)
summary(fm)

## prediction of Stock &amp; Watson's evaluation score
sw &lt;- with(TeachingRatings, mean(beauty) + c(0, 1) * sd(beauty))
names(sw) &lt;- c("Watson", "Stock")
predict(fm, newdata = data.frame(beauty = sw))

## Hamermesh and Parker, 2005, Table 3
fmw &lt;- lm(eval ~ beauty + gender + minority + native + tenure + division + credits,
  weights = students, data = TeachingRatings)
coeftest(fmw, vcov = vcovCL, cluster = TeachingRati