This graduate level course covers the following topics:

• Quick review of discrete probability distributions: binomial, multinomial, and Poisson.
• An introduction to the concept of likelihood.
• Implementing tests for one-way tables using Pearsons X2 and likelihood-ratio G2 statistics.
• Using contingency tables including 2 × 2 and r × c tables, tests for independence and homogeneity of proportions, Fishers exact test, odds ratio and logit, other measures of association.
• Using 3-way tables in full independence and conditional independence contexts, collapsing and understanding Simpson's paradox.
• Using generalized linear models in Poisson regression and logistic regression contexts for dichotomous response, including interpretation of coefficients, main effects and interactions, model selection, diagnostics, and assessing goodness of fit.
• Using polytomous logit models for ordinal and nominal response.
• Using loglinear models (and graphical models) for multi-way tables.
• And other topics as time permits (and due to the interests).  These may include causality, repeated measures, generalized least squares, mixed models, latent-class models, missing data, and/or algebraic statistics approaches.