STK3100 – Introduction to generalized linear models
Schedule, syllabus and examination date
The course gives an overview over important models and techniques for regression analysis outside standard linear regression. In particular, the students will learn how to extend the linear model for response variables with binary/binomial, Poisson and gamma distributions. Furthermore, the students will learn about regression methods for dependent response variables.
After completing the course you will:
- be familiar with the exponential family of distributions and know that the normal, the binomial, the Poisson, and the gamma distributions belong to this family;
- know the class of generalized linear models (GLM) as regression models with responses from the exponential family of distributions;
- be trained in analyzing data from important special cases of GLMs, in particular logistic regression and Poisson regression;
- know the concepts of link functions for modelling the correspondence between the expected value of the responses and covariates and of variance functions for specifying the correspondence between the expected values and variances of the responses;
- be familiar with extensions of the GLM framework using quasi likelihood based on specified link and variance functions;
- know extensions of GLMs that enable modelling and analysis of dependent responses, in particular variance component models and mixed models with both fixed and random components.
Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.
If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.
Formal prerequisite knowledge
In addition to fulfilling the Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:
Mathematics R1 (or Mathematics S1 and S2) + R2
And and in addition one of these:
- Physics (1+2)
- Chemistry (1+2)
- Biology (1+2)
- Information technology (1+2)
- Geosciences (1+2)
- Technology and theories of research (1+2)
The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies (in Norwegian).
Recommended previous knowledge
STK1100 – Probability and statistical modelling, STK1110 – Statistical methods and data analysis 1, STK2120 – Statistical Methods and Data Analysis 2 (discontinued), MAT1100 – Calculus, MAT1110 – Calculus and linear algebra and MAT1120 – Linear algebra.
- 10 credits overlap with STK4100 – Introduction to generalized linear models
- 5 credits overlap with STK4900 – Statistical methods and applications
- 5 credits overlap with STK9900 – Statistical methods and applications
- 10 credits overlap with STK2000 – Some central models and methods in statistic (discontinued)
- 5 credits overlap with STK3900 – Statistical methods and applications (discontinued)
10 credits with and ST-IN216. 5 credits with ST202, ST213 and ST301. 3 credits with ST202A.
* The information about overlaps is not complete. Contact the department for more information if necessary.
3 hours of lectures and 2 hours of topics examined in plenum.
Final written examination.
Examination support material
Approved calculator and formula lists for STK1100/ STK1110 and STK1120.
Language of examination
Subjects taught in English will only offer the exam paper in English.
You may write your examination paper in Norwegian, Swedish, Danish or English.
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Explanations and appeals
Resit an examination
This course offers both postponed and resit of examination. Read more:
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.