Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Multivariate Methods (Level M) STATS5021

  • Academic Session: 2021-22
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes

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.


15 lectures

5 tutorials

10 hours practical sessions

Requirements of Entry

Some optional courses may be constrained by space and entry to these is not guaranteed unless you are in a programme for which this is a compulsory course.

Excluded Courses

STATS4046 Multivariate Methods


90-minute, end-of-course examination (80%)

Coursework (20%)

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;

■ read further into one topic related to the course and use these concepts to solve a real-world problem.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.