Introductory description

The module provides an opportunity to pursue a more advanced area of analytics (building on the core module in Business Statistics). Today organisations and businesses collect and store information in data warehouses, and such information is available to be ‘mined' for improved management decision making. Some of that information can be analysed with simple statistics, but much of it requires more complex, multivariate statistical techniques. The module thus focuses on the use of a range of applied multivariate data analysis techniques to convert information into knowledge.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Multivariate Statistical techniques are important tools of analysis in all fields of management: Finance, Production, Accounting, Marketing, and Personnel Management. In addition, they play key roles in the fundamental disciplines of the social science: Economics, Psychology, Sociology, etc. This course is designed to provide students with a working knowledge of the basic concepts underlying the most important multivariate techniques, with an overview of actual applications in various fields, and with experience in actually using such techniques on a problem of their own choosing. The course will address both the underlying mathematics and problems of applications. The types of tools to be examined include: Multivariate Regression Analysis, Logistic Regression, Principal Component Analysis, Factor Analysis, Discriminant Analysis, and Cluster Analysis.

Learning outcomes

By the end of the module, students should be able to:

• Develop a comprehensive understanding of the different categories of multivariate modelling, and how they are inter-related.
• Critically evaluate the relevant issues and measures available to aid selection of the most suitable models.

Indicative reading list

Chatfield, C. and Collins, A.J. (1989). Introduction to Multivariate Analysis. Chapman and Hall.
Flury, B. and Riedwyl, H. (1989). Multivariate Statistics: a Practical Approach. Chapman and Hall.
Field, A. (2013) Discovering statistics using IBM SPSS statistics, 4th ed. Los Angeles: Sage.
Hair, J.F. et al (2014) Multivariate analysis, 7th ed. Harlow Pearson Education Limited.
Hair, J.F. et al (2018). Multivariate Data Analysis, 8th ed. Pearson International Edition
Hooley, G.J. and Hussey, M.K. (1994). Quantitative Methods in Marketing. Academic Press.
Sharma, S. (1996). Applied Multivariate Techniques. Wiley.
Tabachnick, B.G. & Fidell, L.S. (2018). Using Multivariate Statistics, 7th ed. Pearson.

Subject specific skills

Apply several multivariate statistical models to real data sets, conducting a range of analyses using appropriate software.
Produce reports investigating real data sets and be able to report on the findings from a piece of modelling and analysis in practical term
Select and apply appropriate techniques according to the research context, data type and research question

Transferable skills

Obtain and use numerical and IT skills
Obtain and use problem solving skills
Obtain and use group working skills