# Week 7: Generalized Linear Models, Part 2

## Feb 26-27, 2018

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

### Resources

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

- Mangiafico: Beta regression for percent and proportion data
- Zeileis et al: Beta regression in R
- Prabhakaran: Beta regerssion with R
- Cribari-Neto and Zeileis: Beta regression in R

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:

### Files

### Slides

Slides available on Speakerdeck