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MATH 657 - Categorical Data Analysis |

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. Restrictions: 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 3.000 Credit hours 3.000 Lecture hours 0.000 Lab hours Levels: MN or MC Graduate Schedule Types: Lecture Mathematics Department Restrictions: Must be enrolled in one of the following Levels: MN or MC Graduate Must be enrolled in one of the following Fields of Study (Major, Minor, Concentration, or Certificate): Mathematics and Statistics |

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