Generalised Linear Models - MAT00017H
- Department: Mathematics
- Credit value: 10 credits
- Credit level: H
- Academic year of delivery: 2022-23
Related modules
Additional information
Pre-requisite modules for Natural Sciences students: Statistics Option 1 MAT00033I.
Module will run
Occurrence | Teaching period |
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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
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Understand the unifying role of exponential families when studying the association between response and explanatory variables measured in diverse scales.
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Understand and perform maximum likelihood based inference for GLMs, including in the context of logistic regression, Poisson regression.
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Capability to use the statistical programme R to perform data analysis in the GLM context.
Module content
Syllabus
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Exponential family of distributions and generalised linear models setup, including link functions.[3]
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Model estimation and inference based on (maximum likelihood) asymptotic theory: hypothesis testing, confidence intervals, analysis of deviance.[6]
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Diagnostics, residual checks, interpretation of results, other model selection criteria (e.g. AIC).[4]
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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.