Review of the linear model:  regression, analysis of variance and analysis of covariance.  Extension of the linear model to data from exponential family distributions.  Application to various distributions:  binomial, Poisson, multinomial, exponential, gamma, Weibull.  Models for proportions:  logit, probit and complementary log-log.  The log linear model for counts.  Quasi-likelihood and coping with excess variation.  Models for correlated data.  Fitting models and analyzing data in various software platforms:  R, SPSS, JMP and others.

Prerequisites:  Statistical Theory, Regression

Bibliography

Agresti, A. Foundations of Linear and Generalized Linear Models, Wiley.

Myers, R.H., Montgomery, D.C., Vining, G.G. and Robinson, T.J. Generalized Linear Models with Applications in Engineering and the Sciences, Wiley.