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The University of Southampton

STAT6083 Generalised Linear Models

Module Overview

To introduce the theory of GLMs and apply the models in practice. The module is divided in 5 sections as explained below: Section 1. Introduction: Review of statistical modelling, Linear Regression, Deviance, model checking and regression diagnostics. Section 2. Foundations of GLMs: Foundations of Generalised Linear Models, the exponential family of distributions and its properties, Maximum Likelihood estimation, Score functions and Information, the Newton-Raphson and Fisher scoring algorithms. Section 3. Categorical data and Logistic regression (Binary/Multinomial/Ordinal): One-way contingency tables, two-way contingency tables, measures of association, odds ratios and properties of odds ratios. Binary logistic regression, probit regression, multinomial logistic regression, ordinal logistic regression, Maximum Likelihood Estimation, latent variable approach, deviance, residual analysis and model selection. Section 4. Poisson regression and log-linear models: Models for count data / Poisson regression, Log-linear models for rates, offset terms. Over dispersion and Negative-Binomial regression. Log-linear models for multi-way contingency tables and Simpson’s paradox. Residual analysis, Model selection, Deviance and Likelihood Ratio tests. Section 5. Advanced topics: Robust regression, data driven transformations, Non-parametric regression, kernel, and spline models. Pre-requisite for STAT6079 One of the pre-requisites for STAT6108

Aims and Objectives

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Summarise data with an appropriate statistical model.
  • Use models to describe the relationship between a response and a set of explanatory variables.
  • Interpret the results of the modelling.
  • Use the statistical software package R to fit statistical models.
  • Understand the foundation theory of Generalised Linear Models.
  • Use a range of popular statistical models for continuous and categorical data.


Overview of statistical modelling, linear regression models, one-way contingency tables, quantilequantile plots, two-way contingency tables, log-linear models for rates, dummy variables and interactions, model selection, log-linear models for multi-way contingency tables, logistic regression models, regression diagnostics, multinomial logistic regression models, models for ordinal data, exponential family of distributions, Poisson regression, Negative-binomial regression, non-parametric regression, robust fitting methods, Median regression and M-estimation.

Learning and Teaching

Teaching and learning methods

Teaching will be through a combination of lectures, and computer workshops. Learning activities will include learning in lectures, which will cover explanations of the statistical modelling techniques and their use, as well as by independent study. The computer workshops will provide hands-on experience of the analysis of data and the application of the techniques introduced in the lectures, enabling you to undertake the statistical computing element of the coursework assignment.

Independent Study160
Total study time200

Resources & Reading list

Agresti, A. (2007). An Introduction to Categorical Data Analysis. 

Software requirements. You will require access to R, which is available on the University’s workstations and can be downloaded to your own computer for use with your studies

Fox, J. (1997). Applied Regression Analysis, Linear Models, and Related Methods. 

Dobson, A. J. (2008). An Introduction to Generalized Linear Models. 

On-line resources. There will be a blackboard site where all the module materials (slides, computer worksheets, assignments, list of books, etc.) will be made available.

Agresti, A. (2002). Categorical Data Analysis. 

Fox, J. (2002). An R and S-PLUS Companion to Applied Regression. 



MethodPercentage contribution
Coursework 50%
Exam  (2 hours) 50%


MethodPercentage contribution
Coursework 50%
Exam 50%

Repeat Information

Repeat type: Internal & External

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