Entry requirements

120 credits including 90 credits in statistics.

Learning outcomes

After completing the course the student is expected to

• have an overview of the models that belong to the class of generalised linear models
• be able to use the most common of these models in statistical data analysis in medical and other applications
• be able to determine which model is the most appropriate in different applications
• be able to assimilate the content of scientific articles concerning generalised linear models
• have the ability to both orally and in written form account for results of analyses based on generalised linear models.

Content

Overview of linear statistical models. Generalised linear models: likelihood-based inference. Models with different link-functions and distributions, such as models for discrete data; binary regression; analysis of contingency tables. Introduction to log-linear models. Model estimation. Residual analysis. Practical examples from different application areas.

Instruction

Instruction is given in form of lectures.

Assessment

The examination takes place partly through a written examination at the end of the course and/or through compulsory written and oral assignments.