The University of Southampton

MATH3012 Statistical Methods II

Module Overview

The module Statistical Methods I covers in detail the theory of linear regression models, where explanatory variables are used to explain the variation in a response variable, which is assumed to be normally distributed. However, in many practical situations the data are not appropriate for such analysis. For example, the response variable may be binary, and interest may be focused on assessing the dependence of the probability of 'success' on potential explanatory variables. Such techniques are important in many disciplines such as finance, market research and medicine. Alternatively, a variety of biological and social science data are in the form of cross-classified tables of counts, called contingency tables. The structure of such tables can be examined to determine the pattern of interdependence of the cross-classifying variables.

Aims and Objectives

Module Aims

To cover the theory and application of generalised linear models. This is an extremely broad class of statistical models, which incorporates the linear regression models studied in Statistical Methods I, but also allows binary or count data to be modelled coherently. A series of flexible estimation and model comparison procedures are introduced and used to analyse appropriate data. The interactive statistical computer language R or S-Plus is used throughout

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Recall the definition of a generalised linear model;
  • Estimate coefficients of a generalised linear model using maximum likelihood, interpret the estimates and calculate confidence intervals for the estimates
  • Analyse binary and binomial data using logistic regression models, and other generalised linear models where appropriate
  • Compare generalised linear models using likelihood ratio tests
  • Interpret log-linear models in terms of independence and conditional independence
  • Use exponential family and likelihood theory to derive important results for generalised linear models
  • Assess goodness of fit of a generalised linear model using deviance and residuals
  • Analyse data appropriately using R or S-Plus
  • Use generalised linear models to evaluate predictions and assess the corresponding uncertainty


The module consists of 11 weeks of lectures. The topics for the weeks are roughly as follows: Week 1: Introduction. Revision of distribution and motivation for GLM. Week 2-3: Revision of likelihood based inference/linear models. Weeks 4-8: GLMs: theory and application. Weeks 9-11: Log-Linear models and contingency tables. Likelihood based statistical theory will also be introduced as and when required

Learning and Teaching

Independent Study102
Total study time150

Resources & Reading list

Krzanowski W. An Introduction to Statistical Modelling. 

McCullagh P & Nelder JA. Generalized Linear Models. 

Krause A & Olson M. The Basics of S and S-PLUS. 

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

Dobson AJ. An Introduction to Generalized Linear Models. 

Collett D. Modelling Binary Data. 

Venables WN & Ripley BD. Modern Applied Statistics with S. 

Cox DR & Snell EJ. Analysis of Binary Data. 



MethodPercentage contribution
Coursework 20%
Exam 80%


MethodPercentage contribution
Exam 100%

Linked modules

Pre-requisites: MATH2011 AND MATH2010


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Books and Stationery equipment

Course texts are provided by the library and there are no additional compulsory costs associated with the module

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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