Syllabus for Analysis of Categorical Data
Analys av kategoriska data
Syllabus
- 5 credits
- Course code: 1MS370
- Education cycle: Second cycle
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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
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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.
Content
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.
Instruction
Lectures and computer sessions.
Assessment
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.
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Agresti, ;
Alan,
Categorical Data Analysis
John Wiley & Sons, 2013
Mandatory