770—Categorical Data Analysis. (3) (Prereq: STAT 704 and consent of instructor, or BIOS 759) Advanced methods for analysis of discrete data. Higher-order contingency tables, log-linear and other generalized linear models. Multivariate methods for matched pairs and longitudinal data.
Sample Course Homepage: Recent Semester
Usually Offered: Fall Semesters
Purpose: To develop expertise in description and statistical inference for contingency tables. To extend expertise in constructing and interpreting models for binary response data. To develop expertise in constructing and interpreting log-linear and other generalized linear models for categorical data.
Current Textbook: Categorical Data Analysis, 2nd ed. by A. Agresti. Wiley, 2002.
|Description and inference for two-dimensional contingency tables:
Categorical response data, sampling schemes and distributions, summary measures of association,
large sample inference, exact test for small samples
|Models for binary response variables and generalized linear models:
Logistic regression, logit models, probit models, model diagnostics
|Ordinal and Polytomous Response data: Logit models for ordinal variables, RC models,
Generalized logit models,
cumulative and baseline category logit models
|Log-linear models:Log-linear models for two dimensions, log-linear
models for three or more dimensions, testing goodness of fit, estimation model parameters,
iterative MLEs, hierarchical model fitting, diagnostics
|Dependent samples: Symmetry models, marginal homogeneity, measuring
agreement, case-control models
|Repeated measures:Marginal homogeneity, modeling a repeated categorical
response, modeling a repeated ordinal response, generalized linear models and quasi-likelihood
Notes: Projects to reinforce the concepts will be typical.
Contact Faculty: Joshua Tebbs