Multivariate Methods STATS4046
- Academic Session: 2019-20
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
To provide an appreciation of the types of problems and questions which arise with multivariate data; to give a good understanding of the application of multivariate techniques for the graphical exploration and analysis of multivariate data.
10 hours of practical sessions
Requirements of Entry
The normal requirement is that students should have been admitted to an Honours- or Master's-level programme in Statistics.
STATS5021 Multivariate Methods (Level M)
90-minute, end-of-course examination (80%)
Main Assessment In: April/May
Are reassessment opportunities available for all summative assessments? Not applicable
Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
To provide an appreciation of the types of problems and questions which arise with multivariate data;
to provide a good understanding of the application of classical multivariate techniques for: the graphical exploration of multivariate data, the reduction of dimensionality of multivariate data and analysis in unsupervised and supervised settings.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ display multivariate data in a variety of graphical ways and interpret such displays;
■ apply and interpret methods of dimension reduction including principal component analysis, multidimensional scaling, the biplot, factor analysis, canonical variates;
■ apply and interpret classical methods for cluster analysis and discrimination;
■ use formal criteria for model selection in prediction and model fitting;
■ interpret the output of R procedures for multivariate statistics.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.