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: MA1VM Vectors and Matrices or MA1MM1 Mathematical Methods I or MA1LA Linear Algebra
Non-modular pre-requisites: knowledge of basic probability
Modules excluded:
Module version for: 2017/8

Module Convenor: Dr Karen Ayres


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.

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 statistical software packages 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.
Multi-dimensional scaling.
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 15
Tutorials 4
Practicals classes and workshops 5
Guided independent study 76
Total hours by term 100.00
Total hours for module 100.00

Summative Assessment Methods:
Method Percentage
Set exercise 20
Class test administered by School 80

Other information on summative assessment:
One assignment and one class test.

Formative assessment methods:
Problem sheets and practicals.

Penalties for late submission:
The Module Convenor will apply the following penalties for work submitted late, in accordance with the University policy.

  • where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the 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.

    Length of examination:

    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:
    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: 31 March 2017

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