Unit information: Multivariate Analysis in 2019/20

Unit name	Multivariate Analysis
Unit code	MATH30510
Credit points	10
Level of study	H/6
Teaching block(s)	Teaching Block 2C (weeks 13 - 18)
Unit director	Dr. Kley
Open unit status	Not open
Pre-requisites	MATH11300 Probability 1 and MATH11400 Statistics 1 (or MATH10013 Probability and Statistics), and MATH11005 Linear Algebra and Geometry
Co-requisites	None
School/department	School of Mathematics
Faculty	Faculty of Science

Description including Unit Aims

Unit Aims

To present various aspects of multivariate analysis, covering data exploration, modeling and inference.

Unit Description

Multivariate analysis is a branch of statistics involving the consideration of objects on each of which are observed the values of a number of variables. A wide range of methods is used for the analysis of multivariate data, both unstructured and structured, and this course will give a view of the variety of methods available, as well as going into some of them in detail.

Interpretation of results will be emphasized as well as the underlying theory.

Multivariate techniques are used across the whole range of fields of statistical application: in medicine, physical and biological sciences, economics and social science, and of course in many industrial and commercial applications.

Relation to Other Units

As with the units Linear and Generalized Linear Models and Time Series Analysis, this course is concerned with developing statistical methodology for a particular class of problems.

Applications will be implemented and presented using the statistical computing environment R (used in Probability and Statistics).

Intended Learning Outcomes

Learning Objectives

To gain an understanding of:

• Dimensional reduction and visualisation of high-dimensional datasets;
• Structured and unstructured learning approaches, including classification and clustering;
• Approaches based on notions of similarity/dissimilarity;
• Implementation in the statistical computing environment R.

Transferable Skills

Self assessment by working examples sheets and using solutions provided.

Teaching Information

Lectures (including both theory and illustrative applications), exercises to be done by students.

Assessment Information

100% Examination.

Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.

Reading and References

Recommended

• Christopher Chatfield and Alexander J. Collins, Introduction to Multivariate Analysis. Chapman and Hall, 1986
• W.J. Krzanowski, Principles of Multivariate Analysis: A User's Perspective.Clarendon Press, 1988
• W.J. Krzanowski and F.H.C. Marriott, Multivariate Analysis, Parts I and II. Edward Arnold. 1994
• K.V. Mardia, J.T. Kent, and John Bibby, Multivariate Analysis, Academic Press, 1979