Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 8


 * log DV, log IV
 * polynomial terms and interaction terms both with: I
 * logistic regression
 * create a dummy variable: mpg > mean(mpg)
 * glm(formula, family=binomial("logit"))

two things worth returning to:


 * graphing residuals against fitted values (not just against different values of x)
 * discussing anova better. The key thing I didn't talk about is about different types of "sum of squares" which are discussed in depth on this page hosted by the NIH. By default, SPSS produces Type III standard errors although these very frequently not what you want.
 * Getting the same results in R is easy enough though: library(car); Anova(fit, type="III")