ST744 - Categorical Data Analysis
- Prerequisites: ST512, ST702
- Term & Frequency: Every Spring
- Student Audience: Graduate students in Statistics and related fields
- Credit: 3 credits
- Recent Texts: Categorical Data Analysis, second edition, by Agresti
- Recent Instructors: Daowen Zhang, Fred Wright
- Background and Goals: This course will cover statistical models and methods appropriate for analyzing categorical responses. Special attention will be paid to the analysis of contingency tables, logistic and Poisson regresions, and their extension generalized linear models. Models for repeated categorical data will also be introduced. Asymptotic as well as exact methods and their implementation using statistical software SAS will be discussed.
- Content: Type of categorical data, multinomial distribution, contingency tables, Pearson Chi-square test, Fisher’s Exact test, Mantel-Haenszel test, Cochran-Armitage trend test, independence and conditional independence, Simpson’s paradox, generalized linear models, logistic and Poisson regression models, matched paired studies, McNemar test, conditional logistic regression model and random effects logistic model for data from matched paired studies, models for multinomial data.
- Alternatives: None
- Subsequent Courses: None
S1 2017 Sections:
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