To provide advanced students in statistics, biostatistics with a course of study in the theory and practice of modern extensions of the general linear statistical model.

Advanced topics and types in generalized linear models, structure of data, theory and applications of parameter estimate methods. Logistic regression, Poisson regression, analysis of dependent data, generalized estimating equations, the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, residuals. Model choice, fitting and validation.