Unit information: Multivariate Analysis 34 in 2020/21

Unit name	Multivariate Analysis 34
Unit code	MATHM0510
Credit points	10
Level of study	M/7
Teaching block(s)	Teaching Block 2C (weeks 13 - 18)
Unit director	Dr. Song Liu
Open unit status	Not open
Pre-requisites	MATH11300 Probability 1, MATH11400 Statistics 1 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 units MATH30013 Linear and Generalised Linear Models and MATH33800 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 1 and Statistics 1).

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

The unit will be taught through a combination of

• synchronous online and, if subsequently possible, face-to-face lectures
• asynchronous online materials, including narrated presentations and worked examples
• guided asynchronous independent activities such as problem sheets and/or other exercises
• synchronous weekly group problem/example classes, workshops and/or tutorials
• synchronous weekly group tutorials
• synchronous weekly office hours

Assessment Information

100% Timed, open-book 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