Generalized Linear Models: Theory and Application

About this programme

The course provides advanced knowledge of generalized linear models (GLM). GLM is a collective term for a broad class of regression models characterized by the response variable following a distribution belonging to the exponential family. The connection between the expected value of the response variable and the linear predictor with explanatory variables is made via a so-called link function. GLM includes linear regression as a special case and encompasses models that handle a wide range of data types, such as categorical data, count data, contingency tables, longitudinal, and clustered data. The course forms a part of the Master's Program in Statistics, but it can also be studied as a freestanding course. You will find Tobias' reception hours in the link above. If you want to visit Gebrenegus outside of his reception hours, you are welcome to e-mail him for an appointment. The course is assessed through a knowledge check of the expected learning outcomes. Exam 1 is assessed through a written individual exam. Exam 2 is assessed through a written assignment performed in groups of two students. Exam 2 may be divided into sub-tasks and given continuously during the course in connection with the teaching. An assessment of the individual's performance within the working group must be made possible and documented. To follow your passion. To be close to international career opportunities. To explore city and countryside. In Stockholm you can really get the best of both worlds. Here we explain all the different steps you need to go through when applying to a course or progra