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STAT40150

Academic Year 2024/2025

Multivariate Analysis (STAT40150)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Garrett Greene
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Multivariate analysis considers many response variables simultaneously. This module will cover many of the common techniques used to analyze multivariate data: clustering techniques, classification techniques, ordination techniques such as principal components analysis and graphical techniques such as multidimensional scaling. The emphasis will be on understanding the methodology, applying it using statistical software and the subsequent interpretation of standard output. This course will make use of the free statistical software R (www.r-project.org).

About this Module

Learning Outcomes:

The student will be familiarised with the basic multivariate techniques, why they work, how to use them 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.

Additionally students will be equipped with the necessary transferable skills for statistical analysis in the real world: developing application domain knowledge and presenting reports of analyses.

Indicative Module Content:

Anticipated content:

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

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Computer Aided Lab

11
Specified Learning Activities

32
Autonomous Student Learning

48
Total

115

Approaches to Teaching and Learning:
Lectures, computer labs, problem-based learning.

Requirements, Exclusions and Recommendations
Learning Requirements:

Statistics modules covering inference, regression, maximum likelihood estimation. Some knowledge of vectors and linear algebra including eigenvalues & eigenvectors.


Module Requisites and Incompatibles
Incompatibles:
STAT40340 - Multivariate Analysis, STAT40740 - Multivariate Analysis (Online)


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): 2hr in-person written exam. End of trimester
Duration:
2 hr(s)
 Standard conversion grade scale 40% No
60
 No
Assignment(Including Essay): Data analysis project Week 8 Standard conversion grade scale 40% No
40
 No

Carry forward of passed components
No
 

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.
James, G., Witten, D. Hastie, T. and Tibshirani, R. (2017) An Introduction to Statistical Learning with Applications in R

Name Role
Ms Sinead Mcparland Lecturer / Co-Lecturer
Blerta Begu Tutor
Ms Laura Craig Tutor
Elia Dufossé Tutor
Mr Brian Hassett Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Mon 10:00 - 10:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 16:00 - 16:50
Spring Computer Aided Lab Offering 2 Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 33 Mon 16:00 - 16:50
Spring Computer Aided Lab Offering 2 Week(s) - 32 Mon 16:00 - 16:50
Spring Computer Aided Lab Offering 3 Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Mon 14:00 - 14:50
Spring Computer Aided Lab Offering 4 Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 12:00 - 12:50
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