The University of Southampton
Courses

STAT6083 Generalised Linear Models

Module Overview

To introduce the theory and apply a wide range of statistical models.

Aims and Objectives

Module Aims

By the end of this module, you should be able to perform statistical modelling of relationships between variables (continuous and categorical) with an emphasis on practical and theoretical considerations.

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.

Syllabus

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

TypeHours
Teaching40
Independent Study160
Total study time200

Resources & Reading list

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

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.

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. 

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

Dobson, A. J. (2001). An Introduction to Generalized Linear Models. 

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

Assessment

Summative

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

Referral

MethodPercentage contribution
Coursework 50%
Exam 50%
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