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Course: STAT 34700
Title: Generalized Linear Models
Instructor(s): Peter McCullagh
Course Assistant(s): Danna Zhang and Eric Janofsky
Class Schedule: Sec 01: TR 10:30-11:50AM in Eckhart 133
Office Hours:  
Textbook(s): McCullagh, Nelder, Generalized Linear Models, 2nd edition.
Cox, Analysis of Binary Data, 1st edition.
Cox, Snell, Applied Statistics: Principles and Examples, 1st edition.
Description: This applied course covers factors, variates, contrasts, and interactions; exponential-family models (i.e., variance function); definition of a generalized linear model (i.e., link functions); specific examples of GLMs; logistic and probit regression; cumulative logistic models; log-linear models and contingency tables; inverse linear models; Quasi-likelihood and least squares; estimating functions; and partially linear models.