The generalized linear model (GLM) is a flexible generalization of ordinary least squares regression. The GLM allows the linear model to be related to the response variable via a link function together with an error function. Starting with the familiar linear regression and ANOVA, the course will expand the linear model to include link functions such as the logit with binomial and the log with Poisson error distributions, thereby enabling students to model outcome variables that are not continuous. Attention will be paid to likelihood estimation methods and the checking of model assumptions.
