Generalised Linear Models


How do we model data of very different types in a consistent way? This course explores generalized linear models and illustrates how methods for analysing continuous and categorical data fit into this framework.



  • Semester 1 - 2017

Learning Outcomes

On successful completion of the course students will be able to:

1. Understand and use the principles of statistical modelling;

2. Have a unified conceptual and theoretical framework for many of the most commonly used statistical methods including multiple linear regression, analysis of variance and logistic regression;

3. Develop skills in statistical computing, specifically in the R statistical programming language and R graphics.

4. Write up a report/project on an analysis of a data set and provide a clear report of results with critical interpretation as based on R code, R outputs and theoretical understanding of theory given in the lectures.


Topics include:

  • Linear models
  • Model fitting as an approach to statistical analysis
  • Exponential family of distributions
  • Maximum likelihood estimation
  • Inference methods based on model fitting
  • Generalised Linear Models
  • Models for continuous data (regression analysis of variance)
  • Models for categorical data (logistic regression for nominal and ordinal data, Poisson regression and log-linear models)
  • Generalised Additive models

Assumed Knowledge

STAT2010 Fundamentals of Statistics, STAT2000 Applied statistics and Research Methods

Assessment Items

Written Assignment: Assignment 1

Written Assignment: Assignment 2

Presentation: Oral for Project

Project: Written Project

Formal Examination: Examination - Formal

Contact Hours


Computer Lab

Face to Face On Campus 2 hour(s) per Week for Full Term


Face to Face On Campus 2 hour(s) per Week for Full Term