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. Didelez |
Open unit status | Not open |
Pre-requisites |
Probability 1 (MATH11300), Statistics 1 (MATH 11400) and Linear Algebra and Geometry (MATH 11005) |
Co-requisites |
None |
School/department | School of Mathematics |
Faculty | Faculty of Science |
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.
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.
Syllabus
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).
To gain an understanding of:
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.
The assessment mark for Multivariate Analysis (level M) is calculated as 20% from assessed coursework and 80% from a 1½-hour written examination in May/June.
The coursework requires a facility in the statistical computing environment R. Students should not take this Unit unless they are familiar with R, or confident that they can easily acquire the skills. Please consult the Unit Organiser if in doubt.
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):