The module focuses on traditional normal-theory material which underpins many multivariate statistical methods. In addition, the use of three classical multivariate techniques - principal component analysis, discriminant analysis and cluster analysis - is
considered in some detail. The module also involves some practical data analysis using the statistical software STATA.
Aims and Objectives
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The classical methods of principal components analysis, discriminant analysis, and cluster analysis
- Techniques for displaying and summarising multivariate data
- Basic properties of multivariate distributions and in particular those of the multivariate normal and related distributions
- Standard multivariate hypothesis tests
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- The ability to analyse and solve problems
- The ability to carry out and interpret the results from a principal component analysis, a discriminant analysis, and a cluster analysis
- The use of the statistical software STATA
The module covers traditional normal-theory material and the application of several commonly used multivariate techniques. Topics covered are: simple plotting and display ideas for multivariate data, random vectors and matrices, random sampling from a multivariate population, principal component analysis, the multivariate normal distribution and related distributions (Wishart and Hotelling’s distributions), maximum likelihood estimation, multivariate hypothesis testing, discriminant analysis, measures of
multivariate distance, similarity, and clustering methods.
Learning and Teaching
Teaching and learning methods
This module is delivered through a combination of lectures, tutorials, and computer workshops. The lectures cover the theoretical aspects of the course; practice exercises that complement the lecture material are discussed during the tutorials. The computer workshops involve analysis of data and application of the techniques introduced in the lectures using STATA.
|Total study time||100|
Resources & Reading list
Software requirements. You will require access to the STATA software, which is available on the University’s computer workstations. Note that this software is not currently available for download to your own computer for use with your studies.
B. F. J. Manly (2004). Multivariate Statistical Methods: a Primer. Chapman and Hall/CRC.
R. A. Johnson and D. W. Wichern (2007). Applied Multivariate Statistical Analysis. Pearson Education.
C. Chatfield and A. J. Collins (1980). Introduction to Multivariate Analysis. London:: Chapman and Hall/CRC.
W. J. Krzanowski (2000). Principles of Multivariate Analysis: a User’s Perspective. Oxford: Clarendon.
L. C. Hamilton (2013). Statistics with Stata (Version 12). Cengage.
K. V. Mardia, J. T. Kent and J. M. Bibby (1979). Multivariate Analysis. London: Academic Press.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Repeat type: Internal & External