Week 7: Generalized Linear Models, Part 2

We will continue our exploration of GLMs this week, looking at how GLM’s can be applied to data expressed as zeroes and ones, proportions, rates, and more.

This lecture will be a bit different than others. We will cover many topics in very brief detail. But each of these topics could be its own lecture. Consequently, I will ‘name-drop’ several techniques, simply state what they are and when they might be used, and encourage self-study.

Lecture Topics

  • GLM’s for data expressed as zeroes and ones, proportions, and rates
  • Contrast Bernoulli, Binomial, and Beta regression
  • In brief: Multinomial and cumulative logistic regression (i.e. what to do when Y is categorical)
  • Simulating from the model as a validation step

In-class Activities

As always, this class will have a mixture of R code and lecture slides


Beta regression:… excuse the similar-sounding names. Each of the below articles comes at it a bit differently.

We will use data from the below paper for one exercise. Read if you like:

A good reference for how to run various types of glm in R:




Slides available on Speakerdeck