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# Generalised Linear Models - MAT00017H

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• Department: Mathematics
• Module co-ordinator: Prof. Marina Knight
• Credit value: 10 credits
• Credit level: H
• Academic year of delivery: 2022-23

## Related modules

• None

### Additional information

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

## Module will run

Occurrence Teaching cycle
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

## Assessment

Task Length % of module mark
Closed/in-person Exam (Centrally scheduled)
Generalised Linear Models
2 hours 100

None

### Reassessment

Task Length % of module mark
Closed/in-person Exam (Centrally scheduled)
Generalised Linear Models
2 hours 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 is constantly exploring 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 by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.