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
Learning Outcomes
Having successfully completed this module you will be able to:
- Recall the definition of a generalised linear model;
- 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
- Use exponential family and likelihood theory to derive important results for generalised linear models
- Compare generalised linear models using likelihood ratio tests
- Interpret log-linear models in terms of independence and conditional independence
- 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 |
|---|---|
| Teaching | 48 |
| Independent Study | 102 |
| Total study time | 150 |
Resources & Reading list
Collett D. Modelling Binary Data.
McCullagh P & Nelder JA. Generalized Linear Models.
Cox DR & Snell EJ. Analysis of Binary Data.
Venables WN & Ripley BD. Modern Applied Statistics with S.
Agresti A (2007). An Introduction to Categorical Data Analysis.
Krause A & Olson M. The Basics of S and S-PLUS.
Dobson AJ. An Introduction to Generalized Linear Models.
Krzanowski W. An Introduction to Statistical Modelling.
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.