Unit name | Multivariate Analysis 34 |
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Unit code | MATHM0510 |

Credit points | 10 |

Level of study | M/7 |

Teaching block(s) |
Teaching Block 1B (weeks 7 - 12) |

Unit director | Professor. Jonty Rougier |

Open unit status | Not open |

Pre-requisites |
MATH11300 Probability 1, MATH 11400 Statistics 1 and MATH 11005 Linear Algebra & Geometry. See also Assessment Methods, below. |

Co-requisites |
None |

School/department | School of Mathematics |

Faculty | Faculty of Science |

Unit aims

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. Multivariate techniques are used in medicine, physical, environmental, and biological sciences, economics and social science, and of course in many industrial and commercial applications.

A wide range of methods is used for the analysis of multivariate data, both unstructured and structured, and this course will review some of the more common and useful methods, with emphasis on implementation and interpretation.

General Description of the Unit

For more details, see the course webpage at http://www.maths.bris.ac.uk/~mazjcr/multivariate/home.html

Relation to Other Units

As with the units Linear Models, 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 1 and Statistics 1).

Further information is available on the School of Mathematics website: http://www.maths.bris.ac.uk/study/undergrad/

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.

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

80% Examination and 20% Coursework.

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.

There is no one set text. Any one of the following will be useful, particularly the first one (from which the notation for the course is taken):

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

Transferable Skills

Self assessment by working examples sheets and using solutions provided.