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Generalised Linear Models - MAT00017H

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  • Department: Mathematics
  • Credit value: 10 credits
  • Credit level: H
  • Academic year of delivery: 2022-23

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations


Additional information

Pre-requisite modules for Natural Sciences students: Statistics Option 1 MAT00033I.

Module will run

Occurrence Teaching period
A Autumn Term 2022-23

Module aims

  • To introduce the statistical methodology of generalised linear models (GLM).
  • To perform model selection, estimation and result interpretation for diverse response and explanatory variables, using the GLM methodology.

Module learning outcomes

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

  • Understand and perform maximum likelihood based inference for GLMs, including in the context of logistic regression, Poisson regression.

  • Capability to use the statistical programme R to perform data analysis in the GLM context.

Module content

 

Syllabus

  • Exponential family of distributions and generalised linear models setup, including link functions.[3]

  • Model estimation and inference based on (maximum likelihood) asymptotic theory: hypothesis testing, confidence intervals, analysis of deviance.[6]

  • Diagnostics, residual checks, interpretation of results, other model selection criteria (e.g. AIC).[4]

  • GLMs corresponding to diverse response variables e.g. binary, count, Gamma, using continuous and/or factor covariates and (if appropriate) their interactions.[5]

[ ] approximate number of lectures

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 undergraduate 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.
  • Peter McCullagh, John A Nelder, Generalized Linear Models, Second Edition, Chapman and Hall.


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.