Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 7

• fitting a linear model with one variable: lm()
  • module formulae, which we've already seen!
  • looking at model objects: summary(); m$<tab> or names(m)
    • m$fitted.values;
    • residuals: mtcars$mpg - m$fitted.values OR m$residuals
    • also functions: coefficients(m) (or coef), predict(m), residuals(m) (or resid)
• plotting residuals:
  • hist(residuals(m))
  • plot against our x: plot(mtcars$hp, residuals(m)
  • QQ-plots with qqnorm(residuals(m))
  • doing a plot with ggplot just involves making a dataset: d.fig <- data.frame(hp=mtcars$hp, resids=residuals(m))
• adding controls: just make our formula more complex
  • update.formula()
  • or just a write a new one
  • adding logical variables: no problem!
  • adding categorical variables: no problem! (I'll explain interpretation later, but i want you to see that this works!)
• generating nice regression plots:
  • one of many options: stargazer(m1, m2, type="text") or type="html"