MATH3012 Statistical Methods II

Module Overview

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

Having successfully completed this module you will be able to:

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

Syllabus

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

Teaching and learning methods

Type	Hours
Independent Study	102
Teaching	48
Total study time	150

Resources & Reading list

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

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

Collett D. Modelling Binary Data.

McCullagh P & Nelder JA. Generalized Linear Models.

Krzanowski W. An Introduction to Statistical Modelling.

Cox DR & Snell EJ. Analysis of Binary Data.

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

Dobson AJ. An Introduction to Generalized Linear Models.

Assessment

Summative

Method	Percentage contribution
Coursework	20%
Exam	80%

Referral

Method	Percentage contribution
Exam	100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisites: MATH2010 AND MATH2011

Costs

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 www.calendar.soton.ac.uk.