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2024/25 Taught Postgraduate Module Catalogue

MATH5824M Generalised Linear and Additive Models

15 creditsClass Size: 80

Module manager: Dr Robert G. Aykroyd

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

Pre-requisite qualifications

MATH2715 or MATH2735

This module is mutually exclusive with

MATH3823Generalised Linear Models

This module is approved as an Elective

Module summary

Linear regression is a tremendously useful statistical technique but is limited to normally distributed responses. Generalised linear models extend linear regression in many ways - allowing us to analyse more complex data sets. In this module we will see how to combine continuous and categorical predictors, analyse binomial response data and model count data.A further extension is the generalised additive model. Here, we no longer insist on the predictor variables affecting the response via a linear function of the predictors, but allow the response to depend on a more general smooth function of the predictor.


On completion of this module, students should be able to:
- carry out regression analysis with generalised linear models including the use of link functions, deviance and overdispersion;
- fit and interpret the special cases of log linear models and logistic regression;
- compare a number of methods for scatterpot smoothing suitable for use in a generalised additive model;
- use a backfitting algorithm to estimate the parameters of a generalised additive model;
- interpret a fitted generalised additive model;
- use a statistical package with real data to fit these models to data and to write a report giving and interpreting the results.


Generalised linear model; probit model; logistic regression; log linear models; scatterplot smoothers; generalised addictive model.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours115.00
Total Contact hours35.00
Total hours (100hr per 10 credits)150.00

Private study

Studying and revising of course material.
Completing of assignments and assessments.

Opportunities for Formative Feedback

Regular example sheets.

Methods of assessment

Assessment typeNotes% of formal assessment
In-course Assessment.20.00
Total percentage (Assessment Coursework)20.00

There is no resit available for the coursework component of this module. If the module is failed, the coursework mark will be carried forward and added to the resit exam mark with the same weighting as listed above.

Exam typeExam duration% of formal assessment
Open Book exam2 hr 30 mins80.00
Total percentage (Assessment Exams)80.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 29/04/2024 16:16:34


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