Multivariate Methods

10 credits

Syllabus, Bachelor's level, 1MS003

A revised version of the syllabus is available.

Code: 1MS003
Education cycle: First cycle
Main field(s) of study and in-depth level: Mathematics G2F
Grading system: Pass with distinction (5), Pass with credit (4), Pass (3), Fail (U)
Finalised by: The Faculty Board of Science and Technology, 14 December 2009
Responsible department: Department of Mathematics

Entry requirements

60 credits including Analysis of Regression and Variance

Learning outcomes

In order to pass the course (grade 3) the student should

have a knowledge of methods of visualizing multivariate data sets;

be familiar with the multivariate normal distribution;

know how to perform statistical tests of the mean value vector of a multivariate normal distribution;

know how to perform statistical tests of two or several populations of a multivariate normal distribution;

know methods and techniques for validation of multivariate normal distribution;

be able to use principal component and factor analysis for typical problems;

be able to use canonical correlation analysis;

be able to use classification techniques;

be familiar with methods for multivariate cluster analysis;

be able to 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 (7 credit points) at the end of the course. Assignments and laboratory work (3 credit points) during the course.