Table of Contents:
  • Introduction : distributions and inference for categorical data
  • Describing contingency tables
  • Inference for contingency tables
  • Introduction to generalized linear models
  • Logistic regression
  • Building and applying logistic regression models
  • Logit models for multinomial responses
  • Loglinear models for contingency tables
  • Building and extending loglinear/logit models
  • Models for matched pairs
  • Analyzing repeated categorical response data
  • Random effects : generalized linear mixed models for categorical responses
  • Other mixture models for categorical data
  • Asymptotic theory for parametric models
  • Alternative estimation theory for parametric models
  • Historical tour of categorical data analysis.