STAT 6260

Categorical Data Analysis

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Course Description

Pre-Requisite(s): Graduate standing in Statistics, or instructor permission

This course develops fundamental methodology to the analysis of categorical data. Topics include contingency tables, generalized linear models, logistic regression, and logit and loglinear models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software.


  • Jianhui Zhou

     Rating

     Difficulty

     GPA

    3.85

     Sections

    Last Taught

    Spring 2019

  • Jeffrey Holt

     Rating

     Difficulty

     GPA

    3.85

     Sections

    Last Taught

    Spring 2024

  • Faculty Staff

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Spring 2015