This course will provide an introduction to the principles of generalized regression models, with an emphasis on categorical data models. Categorical data occurs extensively in both observational and experimental studies, as well as in industrial applications. The course will focus on both theory and application of methods for data analysis. Problems will be motivated from a scientific perspective. Topics covered include logistic regression, log-linear models, analysis of deviance, extrabinomial variation, quasi-likelihood, and models for correlated responses.

Upon successful completion of the course, students will be able to:

  • Describe the general structure of a GLM and similarities and differences with linear models
  • Estimate and interpret a logistic regression model
  • Estimate and interpret a Poisson regression model
  • Know of issues and some strategies for dealing with overdispersion in some GLMs
  • Estimate and interpret a GLM for continuous responses that are not normally distributed

Course Information

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Data Sets

R Code

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