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MA50084: Generalised linear models

[Page last updated: 15 October 2020]

Follow this link for further information on academic years Academic Year: 2020/1
Further information on owning departmentsOwning Department/School: Department of Mathematical Sciences
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 1
Further information on unit assessment Assessment Summary: CW 25%, EX 75%
Further information on unit assessment Assessment Detail:
  • Coursework (CW 25%)
  • Examination (EX 75%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Description: Aims & Learning Objectives:
Aims To present the theory and application of normal linear models and generalised linear models, including estimation, hypothesis testing and confidence intervals. To describe methods of model choice and the use of residuals in diagnostic checking. To facilitate an in-depth understanding of the topic.Objectives On completing the course, students should be able to (a) choose an appropriate generalised linear model for a given set of data; (b) fit this model using R, select terms for inclusion in the model and assess the adequacy of a selected model; (c) make inferences on the basis of a fitted model and recognise the assumptions underlying these inferences and possible limitations to their accuracy; (d) demonstrate an in-depth understanding of the topic.

Generalised linear models: Exponential families, standard form, linear predictors and link functions, deviance. Statement of asymptotic theory for the generalised linear model, Fisher information. Vector and matrix representation.
Model building: Subset selection and stepwise regression methods. Effects of collinearity in regression variables. Model checking including residuals AIC and BIC.
Further information on programme availabilityProgramme availability:

MA50084 is Optional on the following programmes:

Department of Mathematical Sciences


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