• Regression covers simple stuff (t-tests) to complex stuff (automated variable selection via penalization) – Yes, I use regression for t-tests Regression is my favorite
Continuous predictors are added directly • Categorical predictors require dummy indicator variables – For each non-reference group, a binary (0 / 1) variable indicating group membership for each subject is created and used in the model Predictors
can be examined using residuals – Look at overall distribution (centered at 0? Skewed? Outliers? – Look at residuals vs predictors (any non-linearity? Trends? Non-constant residual variance?) Diagnostics
linear models • Arguments include – Formula: y ~ x1 + x2 – Data • Output is complex, and also kind of a mess – Use the broom package! Linear models in R