Generalized Lin Models

3 Credits · Level: Graduate, Undergraduate

This is a MS level course. Generalized Linear Models (GLM) provide a unifying statistical framework for analyzing data with a response variable in the exponential family distribution. This course will focus on the theory and application of GLMs. It will cover commonly encountered GLMs, including a review of linear models for normally distributed responses, models for binary outcomes (logistic regression and probit models), models for multinomial outcomes (polytomous regression), models for ordinal responses (proportional odds model), models for count data (Poisson regression, negative binomial regression, zero-inflated models). The statistical software packages SAS and R will be used to illustrate the implementation of these models to data from a wide range of applications.