Generalized linear models
The course introduces models that are various extensions of the linear regression model. These extensions offer greater flexibility for data analysis by allowing other common types of data to be analyzed. The course covers modeling and analysis of binary data, categorical data, proportions, count data, contingency table data, and longitudinal data.
The course provides an overview of several models with a focus on practical applications: being able to select appropriate models based on data, estimate models through programming in R, and interpret and critically evaluate analysis results and predictions with respect to underlying model assumptions. The definition and fundamental theory of the class of Generalized Linear Models (GLM) are covered in the course.
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Course structure
The course is given at day time, full time.
Teaching format
The teaching consists of lectures, exercises, and computer exercises.
Course information
Course description spring 2025 (191 Kb)
Assessment
The course is examined through an individual written exam and a group assignment.
Examiner
Teachers spring 2025
Course coordinator
Teaching assistants
You will find the teachers reception hours in the links above. If you wish to visit them outside of their reception hours, you are welcome to e-mail them for an appointment.
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course. -
Course literature
Note that the course literature can be changed up to two months before the start of the course. -
Contact
Teachers spring 2025
Course coordinator
Teaching assistants
If you have questions about studying at the Department of Statistics, please contact our study- and career counselor.
