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Generalised Linear Models - MAT00103H

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  • Department: Mathematics
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

This module introduces students to setting up models, their estimation and comparisons for diverse response and explanatory variables, departing from the traditional assumptions of normality and linearity.

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations


Module will run

Occurrence Teaching period
A Semester 1 2023-24

Module aims

This module introduces students to setting up models, their estimation and comparisons for diverse response and explanatory variables, departing from the traditional assumptions of normality and linearity.

Module learning outcomes

By the end of the module, students will be able to:

1. Demonstrate the unifying role of exponential families when studying the association between response and explanatory variables measured in diverse scales.

2. Perform maximum likelihood based inference for GLMs, including in the context of logistic and Poisson regression.

3. Provide descriptive statistics and graphical summaries of information contained in data from survival experiments in different types of studies.

4. Use estimation and hypothesis testing for inference for survival data.

5. Use the statistical programme R to perform data analysis in the GLM and survival analysis contexts.

Module content

Data that motivates this module may arise from a variety of fields from medicine to insurance and engineering, each with their characteristic challenges departing from the standard modelling tools. This module will extend your modelling skills from the limited umbrella of linear models to now encompass response data that may be following distributions (such as Binomial, Poisson, Gamma) which are part of the exponential family of distributions and may be non-linearly connected to a set of covariates. This module will also introduce you to modelling survival data, where observations are measured as time to event and often subjected to censoring, e.g. event times are not known completely.

Indicative assessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Module feedback

Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.

Indicative reading

Annette J Dobson, Introduction to Generalized Linear Models, Second Edition, Chapman and Hall.

Klein, J.P. and Moeschberger, M.L. (2003). Survival Analysis: Techniques for Censored and Truncated Data, Second edition, Springer.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.