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Course Topics

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with a certain chemical; and modeling the number of insects caught by a certain kind of trap. These types of situations can often be modeled well by a large class of regression models called generalized linear models (GLM). We will go over some of the basic statistical concepts of GLM and how it is relates to regression using normal errors. We will also go through some data analysis examples of GLMs in popular software such as R and SAS (possibly JMP if time allows) and explain how we interpret some of the output from each software. If time allows, the Bayesian approach to GLM will also be discussed.

[video:https://vimeo.com/31795498]

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