Practical Multivariate Analysis - MA781

Location Term Level Credits (ECTS) Current Convenor 2017-18 2018-19
Canterbury Spring
View Timetable
6 15 (7.5) PROF JE Griffin

Pre-requisites

MA629, MA632

Restrictions

None

2017-18

Overview

This module considers statistical analysis when we observe multiple characteristics on an experimental unit. For example, a sample of students' marks on several exams or the genders, ages and blood pressures of a group of patients. We are particularly interested in understanding the relationships between the characteristics and differences between experimental units. Outline syllabus includes: measure of dependence, principal component analysis, factor analysis, canonical correlation analysis, hypothesis testing, discriminant analysis, clustering, scaling.

Details

This module appears in:


Contact hours

This module is organised in conjunction with the Multivariate Analysis (MA079) course which is a
compulsory part of the MSc in Statistics. Students will attend about 18 lectures, with computer-based illustrations included as appropriate rather than treated separately. The remainder of the work will be arranged on an individual basis.

Method of assessment

70% Examination, 30% Coursework

Preliminary reading

KV Mardia, JE Kent and JM Bibby Multivariate Analysis, Academic Press, London, 1979
C Chatfield and AJ Collins Introduction to Multivariate Analysis, Chapman and Hall, 1980
DF Morrison Multivariate statistical methods, 4th ed., Duxbury, 2005

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

The Intended Subject Specific Learning Outcomes. On successful completion of the module students
– will appreciate the range of multivariate techniques currently available,
– will be able to summarise and interpret multivariate data,
– will have a clear understanding of the logical link between multivariate techniques and corresponding univariate techniques, where appropriate,
– will be able to use multivariate techniques appropriately,
– will appreciate the opportunities for using statistical techniques of multivariate analysis to summarise and interpret complex sets of data,
– will be able to undertake standard multivariate hypothesis tests, and draw appropriate conclusions.

The Intended Generic Learning Outcomes. On successful completion of the module, students
– will have further developed a logical, mathematical approach to solving problems,
– will have enhanced their ability to work with relatively little guidance,
– will have gained further organisational and study skills.
On successful completion of the module, students will also have improved their key skills in written communication, numeracy, problem solving and information technology.

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