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UNDERGRADUATE PROGRAM | |
GRADUATE PROGRAM |
Course Homepage: Spring 2004
Usually Offered: Spring Semester in even years
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.
| Topics Covered | Chapters | Time |
| 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 |
1-3 | 2 weeks |
| Models for binary response variables
and generalized linear models: Logistic regression, logit models, probit models, model diagnostics |
4-6 | 3 weeks |
| Ordinal and Polytomous Response data: Logit models for
ordinal variables, RC models, Generalized logit models, cumulative and baseline categorty logit models |
7 | 2 weeks |
| 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 |
8 | 2 weeks |
| Dependent samples: Symmetry models,
marginal homogeneity, measuring agreement, case-control models |
10 | 2 weeks |
| Repeated measures:Marginal
homogeneity, modeling a repeated categorical response, modeling a repeated ordinal response, generalized linear models and quasi-likelihood |
11 | 2 weeks |
Notes: Projects to reinforce the concepts will be typical.
Contact Faculty: John Grego
(Last Updated: August 14th, 2006)
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