STK3100 – Introduction to Generalized Linear Models
Schedule, syllabus and examination date
Changes in the course due to coronavirus
Autumn 2020 we plan for teaching and examinations to be conducted as described in the course description and on semester pages. However, changes may occur due to the corona situation. You will receive notifications about any changes at the semester page and/or in Canvas.
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 modeling 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 modeling and analysis of dependent responses, in particular variance component models and mixed models with both fixed and random components.
Admission to the course
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.
Special admission requirements
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:
Information technology (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
- MAT1100 – Calculus
- MAT1110 – Calculus and Linear Algebra
- MAT1120 – Linear Algebra
- 10 credits overlap with STK2000 – Some central models and methods in statistic (discontinued).
- 10 credits overlap with ST-IN216.
- 10 credits overlap with STK4100 – Introduction to Generalized Linear Models.
- 5 credits overlap with STK3900 – Statistical methods and applications (discontinued).
- 5 credits overlap with STK4900 – Statistical Methods and Applications.
- 5 credits overlap with ST301.
- 5 credits overlap with ST301.
- 5 credits overlap with ST202.
- 5 credits overlap with ST213.
- 5 credits overlap with STK9900 – Statistical Methods and Applications.
- 3 credits overlap with ST202A.
5 hours of lectures/exercises per week throughout the semester.
The course may be taught in Norwegian if the lecturer and all students at the first lecture agree to it.
Final written exam 4 hours which counts 100 % towards the final grade.
This course has 2 mandatory assignments that must be approved before you can sit the final exam.
It will also be counted as one of the three attempts to sit the exam for this course, if you sit the exam for one of the following courses: STK4100 – Introduction to Generalized Linear Models
Examination support material
Approved calculators are allowed. Information about approved calculators in Norwegian.
Language of examination
Courses 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.
Resit an examination
This course offers both postponed and resit of examination. Read more: