# Course Catalogue

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

• 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.

### Timetable

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

### Assessment

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.