ST4MVA-Multivariate Data Analysis

Module Provider: Mathematics and Statistics
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites: MA1LA Linear Algebra
Non-modular pre-requisites:
Co-requisites:
Modules excluded: ST3MVA Multivariate Data Analysis
Current from: 2022/3

Module Convenor: Dr Julia Abery
Email: j.abery@reading.ac.uk

Type of module:

Summary module description:

This module focusses on exploratory multivariate data analysis techniques, 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. 

Aims:

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; and 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 the results;


• the ability to independently research multivariate data analysis techniques and learn how to apply them;


• the ability to use statistical software packages for the analysis of multivariate data.

This module will be assessed to a greater depth than the excluded module ST3MVA.

Additional outcomes:

Outline content:

• Graphical techniques to show multivariate data.


• Principal component analysis.


• Factor analysis.


• Cluster analysis.


• Canonical variate analysis; discriminant functions; canonical correlation.


• Correspondence analysis; biplots; singular value decomposition.


• Use of a software package for multivariate data analysis.

Brief description of teaching and learning methods:

Lectures supported by problem sheets, tutorials and PC practicals.

Contact hours:

	Autumn	Spring	Summer
Lectures	14
Tutorials	5
Practicals classes and workshops	5
Guided independent study:	76
Total hours by term	100	0	0
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Written assignment including essay	100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

One extended data analysis assignment.

Formative assessment methods:

Feedback given during tutorials and PC classes, as well as engaging with problem sheets and the solutions.

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: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
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 50% overall.

Reassessment arrangements:

A further extended data analysis assignment.

Additional Costs (specified where applicable):

1) Required text books: 

2) Specialist equipment or materials: 

3) Specialist clothing, footwear or headgear: 

4) Printing and binding: 

5) Computers and devices with a particular specification: 

6) Travel, accommodation and subsistence: 

Last updated: 22 September 2022