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Unit information: Multivariate Analysis in 2020/21

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. Cho
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

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 details

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 Details

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

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