Entry requirements

120 credits including Analysis of Regression and Variance. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

• have acquired a good overview of linear statistical models and their generalisations;
• be acquainted with the theory of generalised linear models;
• be able to use models with various link functions and link distributions such as models for discrete data;
• be able to perform binary logistic regression and analysis of contingency tables;
• be familiar with log-linear models;
• be familiar with quasi-likelihood methods;
• be able to analyse a given set of data using generalised linear models;
• have experiences of practical examples from various areas of applications, especially medical applications.

Content

Linear statistical models, generalised linear models. Likelihood-based inference. Models for discrete data. Logistic regression. Analysis of contingency tables. Introduction to log-linear models. Estimation and model fitting. Residual analysis. Quasi-likelihood methods. Practical examples from different application areas with emphasis on medical applications.

Instruction

Lectures, problem solving sessions and computer-assisted laboratory work.

Assessment

Written examination at the end of the course. Compulsory assignments and laboratory work during the course.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.