Syllabus for Generalised Linear Models

Generaliserade linjära modeller

A revised version of the syllabus is available.


  • 5 credits
  • Course code: 1MS369
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Mathematics A1N
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2016-03-10
  • Established by: The Faculty Board of Science and Technology
  • Applies from: week 30, 2016
  • Entry requirements: 120 credits including 90 credits mathematics with Regression analysis
  • Responsible department: Department of Mathematics

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.


Models with different link functions. Binary (logistic) regression, Estimation and model fitting. Residual analysis. Mixed effext models. Hierarchical models. Practical examples. R commands.


Lectures and computer sessions.


Written examination at the end of the course. Complulsory assignments during the course.

Syllabus Revisions

Reading list

Reading list

Applies from: week 31, 2016

  • Madsen, Henrik; Thyregod, Poul Introduction to general and generalized linear models

    Boca Raton, Fla.: CRC, cop. 2011

    Find in the library