STAT40740 Multivariate Analysis (Online)

Academic Year 2023/2024

This module will cover many common statistical techniques used to analyse high dimensional data. Topics include: clustering techniques; classification techniques; ordination techniques such as principal components analysis; and graphical techniques such as multidimensional scaling. All analyses will be conducted using the R software.

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Curricular information is subject to change

Learning Outcomes:

The student will be familiarised with the basic multivariate techniques and where their use is appropriate. The student will develop skills to conduct an analysis of multivariate data using statistical software, interpret the results and draw conclusions. The student will be made aware of the advantages and limitations of each method.

Indicative Module Content:

Anticipated content:

Introduction to multivariate data.
Mathematical necessities.
Multidimensional scaling
Principal components analysis
Factor analysis

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning


Online Learning




Approaches to Teaching and Learning:
Lectures, tutorials, enquiry and problem-based learning. 
Requirements, Exclusions and Recommendations
Learning Requirements:

Basic statistics modules covering e.g. hypothesis testing, inference, regression, maximum likelihood. Elementary matrix algebra including eigenvalues and eigenvectors.

Module Requisites and Incompatibles
STAT40150 - Multivariate Analysis, STAT40340 - Multivariate Analysis

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Online assessments Throughout the Trimester n/a Alternative linear conversion grade scale 40% No


Examination: Written online exam 2 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Everitt, B. S. An R and S-PLUS Companion to Multivariate Analysis
Härdle, W.and Simar, L. Applied Multivariate Statistical Analysis.
Everitt, B. and Hothorn, T. An introduction to applied multivariate analysis with R.
Johnson, R. and Wichern, D. Applied Multivariate Statistical Analysis.
Venables, W. and Ripley, B. Modern Applied Statistics with S.
Lattin, J., Carroll, J., and Green, P. Analyzing Multivariate Data.
Everitt, B. and Hothorn, T. A Handbook of Statistical Analyses Using R.
Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction.
Name Role
Faezeh Fadaei Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.

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