Syllabus for Analysis of Categorical Data

Analys av kategoriska data


  • 5 credits
  • Course code: 1MS370
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Mathematics A1N
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2016-03-10
  • Established by:
  • Revised: 2022-02-16
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2022
  • Entry requirements:

    120 credits including 90 credits in mathematics. Regression Analysis participation. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Mathematics

Learning outcomes

On completion of the course, the student should be able to:

  • give an account of the sampling strategies for categorical data,
  • analyse a two-way contingency table,
  • carry out exact inference for a three-way contingency table,
  • build and apply logit and loglinear models,
  • use R for analysing real data sets,
  • be able to interpret the results in practical examples.


Poisson sampling. Binomial sampling. Inference for odds ratio. Chi-squared tests. Fisher's exact test. Partial tables. Cochran-Mantel-Haenszel methods. Exact tests. Models for binary data. Loglinear models for contingency tables. R commands.


Lectures and computer sessions.


Written examination at the end of the course (4 credits). Written and oral presentation of a project (1 credit).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

Reading list

Applies from: Autumn 2022

Some titles may be available electronically through the University library.

  • Agresti, ; Alan, Categorical Data Analysis

    John Wiley & Sons, 2013

    Find in the library


Last modified: 2022-04-26