Course Catalogue

Multivariate Methods STATS4046

  • Academic Session: 2026-27
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 1
  • Available to Visiting Students: Yes
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

To provide an appreciation of the types of problems and questions which arise with multivariate data; to give a good understanding of the application of multivariate techniques for the graphical exploration and analysis of multivariate data.

Timetable

15 lectures

5 tutorials

10 hours of practical sessions

Requirements of Entry

The normal requirement is that students should have been admitted to an Honours- or Master's-level programme in Statistics.

Excluded Courses

STATS5021 Multivariate Methods (Level M)

Assessment

90-minute, end-of-course examination (85%)

Coursework (15%)

Main Assessment In: April/May

Course Aims

To provide an appreciation of the types of problems and questions which arise with multivariate data;

to provide a good understanding of the application of classical multivariate techniques for: the graphical exploration of multivariate data, the reduction of dimensionality of multivariate data and analysis in unsupervised and supervised settings.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ display multivariate data in a variety of graphical ways and interpret such displays;

■ apply and interpret methods of dimension reduction including principal component analysis, multidimensional scaling, the biplot, factor analysis, canonical variates;

■ apply and interpret classical methods for cluster analysis and discrimination;

■ use formal criteria for model selection in prediction and model fitting;

■ interpret the output of R procedures for multivariate statistics.

Minimum Requirement for Award of Credits

No exceptions