MATH-657 Categorical Data Analysis
Spring for 2016-2017
This course deals with statistical models for the analysis of categorical data. Topics to be covered include inference for contingency tables, generalized linear models with emphasis on logistic regression, Poisson regression, and models for clustered/repeated measures.. The goal of the course is not to memorize formulae, but to understand and apply statistical concepts and techniques to real data.

Prerequisites: Background in maximum likelihood theory and linear models are required (at the level of Math-651).

Textbook: Categorical Data Analysis, 3rd Edition by Alan Agresti, ISBN: 978-0-470-46363-5.

Must be enrolled in one of the following Levels:
MN or MC Graduate
Must be enrolled in one of the following Majors:
Mathematics and Statistics

Credits: 3
Prerequisites: MATH-503, and MATH-651 or BIST-512
More information
Look for this course in the schedule of classes.

The academic department web site for this program may provide other details about this course.