- Department: Mathematics
- Credit value: 10 credits
- Credit level: M
- Academic year of delivery: 2022-23
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
Occurrence | Teaching period |
---|---|
A | Spring Term 2022-23 |
The aim of the module is to introduce students to the main ideas and their justifying theories of multivariate statistical analysis.
To have developed a knowledge and good understanding of models and methods for multivariate data;
To have a good degree of familiarity with the main methodologies and techniques of multivariate analysis;
To know what sorts of methodologies should be applied to different sets of multivariate data;
To use statistical package R to analyze multivariate data by various methodologies;
To have a reasonable degree of familiarity with the main mathematical statistical theory of multivariate analysis;
Task | % of module mark |
---|---|
Closed/in-person Exam (Centrally scheduled) | 90 |
Coursework - extensions not feasible/practicable | 10 |
None
Task | % of module mark |
---|---|
Closed/in-person Exam (Centrally scheduled) | 100 |
Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.
Richard Johnson, Dean Wichern. Applied Multivariate Statistical Analysis. Prentice Hall, ISBN 0-1312-1973-1. (SF 2 JOH)
Brian Everitt. An R And S-plus Companion To Multivariate Analysis. Springer, 2005. (SF 2 EVE).
C Chatfield and A J Collins. Introduction to Multivariate Analysis. Chapman and Hall (SF 2 CHA).
K V Mardia, J T Kent and J M Bibby. Multivariate Analysis. Academic Press (SF2 MAR).
T.W. Anderson. An introduction to multivariate statistical analysis. New York : Wiley, 1984.
M G Kendall. Multivariate Analysis. Arnold (SF 2 KEN).