Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

Generalized Linear Models: Course Outline

In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution.

E-Books (Full Text)

Course Outline

  • Logit model for dichotomous data with  single  and  multiple explanatory variables ,
  • maximum likelihood (ML)  estimation,
  • large sample tests about parameters ,
  • good of fit , 
  • analysis  of deviance ,
  • variable selection ,
  • extension to polytomous data ,
  • introduction to Poisson regression.
  • Linear models for two and three dimensional contingency tables:
  • interpretation of parameters,
  • comparison with ANOVA and regression.
  • ML estimation of parameters.
  • Likelihood ratio tests for various hypotheses,
  • including independence,
  • marginal and conditional independence,
  • partial association .
  • Models with quantitative levels.
  • Nonparametric regression and generalized linear models.
  • Interpolating and smoothing splines for simple regression.
  • Use of cross-validation Applications to Logistic and Poisson regression.
  • Additive models and generalized additive models.   

E-Books (Full Text)