University of St Andrews

Key information

SCOTCAT credits : 15

ECTS credits : 7

Level : SCQF level 11

Semester: 2

Availability restrictions: Not automatically available to General Degree students

Planned timetable: 11.00 am Mon (even weeks), Tue and Thu

This module provides theory and application for the analysis of multivariate data. Fundamental matrix material is presented including mean vectors, covariance matrices, correlation matrices and basic properties of multivariate normal distributions. Multivariate extensions to common univariate tests are subsequently covered. Distance metrics and general measures of similarity are explored, leading to the broader utility of multivariate methods in real-world problems, particularly for classification and dimension reduction. The most common and fundamental methods are covered, including Principal Components Analysis, multidimensional scaling, clustering and discriminant analyses. The practical component of the module focuses on analysis of real data using widespread software.

Relationship to other modules

Pre-requisite(s): The student must have been accepted on to MMath statistics or MMath mathematics programmes.

Anti-requisite(s): You cannot take this module if you take MT4609

Learning and teaching methods and delivery

Weekly contact: 2.5 lectures (weeks 1 - 10), and 4 tutorials and 4 project group meetings over the semester.

Scheduled learning hours: 33

Guided independent study hours: 117

Assessment pattern

As used by St Andrews: 2-hour Written Examination = 50%, Coursework = 50%

As defined by QAA
Written examinations : 50%
Practical examinations : 0%
Coursework: 50%

Re-assessment: 2-hour Written Examination = 100%

Personnel

Module coordinator: Dr V M Popov
Module teaching staff: Dr Valentin Popov, Dr Steven Drasco