Generalised Linear Models
Syllabus, Master's level, 2ST075
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Statistics A1N
- Grading system
- Fail (U), Pass (G), Pass with distinction (VG)
- Finalised by
- The Department Board, 23 October 2014
- Responsible department
- Department of Statistics
120 credits including 90 credits in statistics.
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.
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 is given in form of lectures.
The examination takes place partly through a written examination at the end of the course and/or through compulsory written and oral assignments.