Generalised Linear Models
5 credits
Syllabus, Master's level, 1MS369
A revised version of the syllabus is available.
- Code
- 1MS369
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Mathematics A1N, Mathematics A1N
- Grading system
- Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Finalised by
- The Faculty Board of Science and Technology, 10 March 2016
- Responsible department
- Department of Mathematics
Entry requirements
120 credits including 90 credits mathematics with Regression analysis
Learning outcomes
In order to pass the course the student should be able to
- give an account of the idea of generalising of linear modelling;
- find the right link function
- apply the maximum likelihood inference to general linear models;
- give an account of the quasi likelihood approach;
- carry out tests in general linear models;
- use R for analysing real data sets;
- be able to interpret the results in practical examples.
Content
Models with different link functions. Binary (logistic) regression, Estimation and model fitting. Residual analysis. Mixed effext models. Hierarchical models. Practical examples. R commands.
Instruction
Lectures and computer sessions.
Assessment
Written examination at the end of the course. Complulsory assignments during the course.