ST3MVA-Multivariate Data Analysis

Module Provider: Mathematics and Statistics
Number of credits: 10 [5 ECTS credits]
Terms in which taught: Autumn term module
Pre-requisites: MA1LA Linear Algebra or MA1LANU Linear Algebra
Non-modular pre-requisites: knowledge of basic probability
Modules excluded: ST4MVA Multivariate Data Analysis
Current from: 2021/2

Module Convenor: Mrs Julia Abery

Type of module:

Summary module description:

This module introduces methods for the analysis of data involving several measurements, where the aim is to identify similarities and differences between observations based on several variables. Multivariate data analysis techniques have a long history of being applied to analyse data from a wide range of disciplines such as psychology, and marketing and research. This module will introduce several techniques covering the underlying theory, as well as carrying out the analysis using software and interpreting the results.

In many experiments or surveys, several different variables are recorded for each of many individuals. The problems associated with this sort of data can be tackled using multivariate data analysis techniques. The methods to be discussed will be descriptive in nature and include the following topics: principal component analysis; canonical variates analysis; cluster analysis; factor analysis. The theory of the methods will be shown, together with how to apply them in practice.

Assessable learning outcomes:

By the end of the module it is expected that the student will have:

  • knowledge of the role of multivariate data analysis in statistics;

  • the ability to identify, justify and explain the most appropriate statistical techniques for a multivariate dataset;

  • the ability to carry out commonly used multivariate data analysis techniques, and interpret results;

  • the ability to use an appropriate st atistical software package for the analysis of multivariate data.

Additional outcomes:

Outline content:

  • Graphical techniques to show multivariate data.

  • Principal component analysis; factor analysis.

  • Cluster analysis.

  • Canonical variate analysis; discriminant functions.

  • Correspondence analysis; biplots; singular value decomposition.

  • Use of a statistical package for multivariata data analysis.

Brief description of teaching and learning methods:
Lectures supported by problem sheets, tutorials and PC practicals.

Contact hours:
  Autumn Spring Summer
Lectures 15
Tutorials 4
Practicals classes and workshops 5
Guided independent study: 76
Total hours by term 100
Total hours for module 100

Summative Assessment Methods:
Method Percentage
Written exam 80
Set exercise 20

Summative assessment- Examinations:

2 hours

Summative assessment- Coursework and in-class tests:

One assignment.

Formative assessment methods:
Problem sheets and practicals.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

  • where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
  • where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:
A mark of at least 40% overall.

Reassessment arrangements:
One examination paper of 2 hours duration in August/September - the resit module mark will be the higher of the exam mark (100% exam) and the exam mark plus previous coursework marks (80% exam, 20% coursework).

Additional Costs (specified where applicable):

1) Required text books: None

2) Specialist equipment or materials: None

3) Specialist clothing, footwear or headgear: None

4) Printing and binding: None

5) Computers and devices with a particular specification: None

6) Travel, accommodation and subsistence: None

Last updated: 22 July 2021


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