Multivariate Methods

Entry requirements

60 credits in mathematics including Probability Theory I and Inference Theory I.

Learning outcomes

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

• give an account of some methods of visualizing multivariate data sets;
• give an account of and use multivariate normal distribution;
• perform statistical tests of the mean value vector of a multivariate normal distribution;
• perform statistical tests of two or several populations of a multivariate normal distribution;
• give an account of methods and techniques for validation of multivariate normal distribution;
• use principal component and factor analysis for typical problems;
• use canonical correlation analysis;
• use classification techniques;
• give an account of and use methods for multivariate cluster analysis;
• present mathematical statistical arguments to others.

Content

Methods of visualisation. Multivariate normal distribution, test of mean value vector, test of one or several populations. Techniques for validation. Principal component analysis. Factor analysis. Canonical correlation analysis. Classification. Multivariate cluster analysis.

Instruction

Lectures, problem solving sessions and computer-assisted laboratory work.

Assessment

Written examination ( 8 credit points) at the end of the course as well as assignments, laboratory work and, possibly, oral presentation during the course ( 2 credit points) in accordance with instructions at course start.

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.