MT5758 Multivariate Analysis

Academic year

2024 to 2025 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Not automatically available to General Degree students

Planned timetable

11.00 am Mon (even weeks), Tue and Thu

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr E Mirfarah

Dr E Mirfarah
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Giorgos Minas

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

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-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT3507 OR PASS MT3508

Anti-requisites

YOU CANNOT TAKE THIS MODULE IF YOU TAKE MT4609

Assessment pattern

2-hour Written Examination = 50%, Coursework = 50%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 lectures (x 10 weeks), and 4 tutorials and 4 project group meetings over the semester.

Scheduled learning hours

33

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

117

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Gain an intuitive understanding of central concepts and operations in matrix algebra such as matrix multiplication, eigenvectors and eigenvalues
  • Learn techniques for exploratory data analysis, in particular for data visualisation
  • Understand and apply Principal Component Analysis
  • Learn techniques for dimension reduction based on distances between points (Multi-dimensional scaling)
  • Understand and apply methods for grouping observations (Cluster Analysis)
  • Understand and apply techniques for reducing the number of relationships between variables (Multivariate Regression)