Module manager: tbc
Email: tbc
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
None
| MATH2701M |
| MATH3702 | Multivariate Analysis and Classification |
| MATH3772 | Multivariate Analysis |
| MATH5745 | |
| MATH5772 |
MATH5772M Multivariate and Cluster Analysis MATH5745 Multivariate Methods
This module is not approved as an Elective
In today’s data-driven world, almost every data set is multivariate. It is typical that experimental units or individuals are measured on more than one variable at a time. The purpose of multivariate analysis is to develop methods to uncover underlying structures and patterns in complex multivariate data to enhance the understanding and interpretation of how multiple variables interact in multivariate data.
This module will teach students the theory and methods to analyse multivariate data. Some methods, such as estimation and testing for the multivariate normal distribution, can be viewed as extensions of methods for univariate continuous data. Other methods are specific to the multivariate setting. These include methods for dimension reduction (principal component analysis, factor analysis, and multidimensional scaling) and methods to classify individuals into groups (discriminant analysis, cluster analysis). Computer software will be used to perform analyses and communicate results with applications to real data.
Subject specific learning outcomes:
summarize multivariate data in terms of the mean vector and covariance/correlation matrices
construct joint, marginal and conditional distributions, with particular emphasis on the multivariate normal distribution;
obtain and use Hotelling's T-squared statistic for one-sample and two-sample testing problems;
motivate and carry out principal component analysis and factor analysis to reduce the number of variables in multivariate data;
derive and interpret decision rules in discriminant analysis;
partition data into clusters using mixture models and hierarchical methods.
use multidimensional scaling to construct low-dimensional representations of data;
analyse real data using a statistical package and write a report giving and interpreting the results
Skills learning outcomes:
On successful completion of the module students will be able to:
(a) Make a critical assessment of varied data sets
(b) Follow a logical approach for selecting and performing an analysis
(c) Use IT skills and appropriate digital technology in work and studies
(d) Reflect on statistical findings and draw relevant conclusions in academic and practical contexts
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Practicals | 1 | 2 | 2 |
| Lecture | 33 | 1 | 33 |
| Private study hours | 115 | ||
| Total Contact hours | 35 | ||
| Total hours (100hr per 10 credits) | 150 | ||
115
Formative feedback will be provided on regular example sets or other similar learning activity.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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