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 |
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).
Learning Objectives
To gain an understanding of:
Transferable Skills
Self assessment by working examples sheets and using solutions provided.
The unit will be taught through a combination of
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.
Recommended