Discrete Models (DisMod)

Course content

Introduction to and analysis of a number of statistical models for discrete response variables: contingency tables, loglinear models, graphical models, smooth hypotheses in the multinomial distribution, Poisson regression, logistic regression and proportional-odds models, survey sampling.


MSc Programme in Statistics

Learning outcome

At the end of the course the student will have knowledge about different types of discrete models, the mathematical relationships between them, and basic statistical properties of the models. The student will have the knowledge to
* explain the asymptotic test theory for models for contingency tables,
* explain the logistic regression model, in the fundamental version for binary responses, as well as in the modifications for response variables with several possible outcomes
* explain the theory for stratified survey sampling and multistage survey sampling
* explain fundamental concepts within the theory of graphical models

The student will acquire the skills to apply discrete models to real data, decide on which model to use and which analysis to perform. The student will have the skills to utilize theoretical results in the practical analysis, including how complex models can be specified by use of several covariates.

At the end of the course the students will have the competence to
* carry out the analysis of 2-way and, more generally, k-way contingency tables, theoretically as well as in practice.
* carry out practical analysis of simple graphical models.
* conduct the practical analysis of complex regression models with response variables with a small number of possible outcomes.
* carry out practical survey analysis in simple situations.



4 hours of lecturing, 4 hours of exercise classes per week for 7 weeks

Statistik 2 (Stat2) or similar.

7,5 ECTS
Type of assessment
Written assignment, 3 days
3 days written take-home assignment
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
One internal examiner for the ordinary exam.
Several internal examiners for the oral re-exam.
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome.

Single subject courses (day)

  • Category
  • Hours
  • Exam
  • 45
  • Preparation
  • 105
  • Lectures
  • 28
  • Practical exercises
  • 16
  • Theory exercises
  • 12
  • English
  • 206