STK3100 – Introduction to Generalized Linear Models

Schedule, syllabus and examination date

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Course content

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.

Learning outcome

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 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, STK2120 – Statistical Methods and Data Analysis 2 (discontinued), MAT1100 – Calculus, MAT1110 – Calculus and Linear Algebra and MAT1120 – Linear Algebra.

Overlapping courses

*The information about overlaps for discontinued courses may not be complete. If you have questions, please contact the Department. 


3 hours of lectures and 2 hours of topics examined in plenum.


2 mandatory assignments.

Final written examination.


Examination support material

Approved calculator.

Information about approved calculators (Norwegian only)

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.

Grading scale

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.

Facts about this course






Every autumn


Every autumn

Teaching language


The course may be taught in Norwegian if the lecturer and all students at the first lecture agree to it.