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Course: STAT 34700
Title: Generalized Linear Models
Instructor(s): Yali Amit
Teaching Assistant(s): Minzhe Wang, Jason Willwerscheid, and Fengshuo Zhang
Class Schedule: Sec 01: TR 2:00–3:20 PM in Stuart 101
Office Hours:  
Textbook(s): Faraway, Extending the Linear Model with R, 2nd edition.
Description: This course covers exponential-family models; definition of generalized linear models; specific examples of GLMs; logistic and probit regression; cumulative logistic models; log-linear models and contingency tables; Quasi-likelihood and least squares; estimating functions; survival analysis; linear mixed models and generalized linear mixed models; and derivation of the methods are presented including likelihood analysis and some basic asymptotic properties. The course emphasizes the use and interpretation of generalized linear models with the R package. Techniques discussed are illustrated by examples involving physical, biological, and social science data.

Prerequisite(s): STAT 34300 or consent of instructor