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.