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 Type	Hours
Lectures	24
Computer Aided Lab	11
Specified Learning Activities	32
Autonomous Student Learning	48
Total	115

Learning Requirements:

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

Incompatibles:
STAT40340 - Multivariate Analysis, STAT40740 - Multivariate Analysis (Online)


 

Assessment Strategy  

Description	Timing		Component Scale		% of Final Grade
Examination: Examination	2 hour End of Trimester Exam	No	Alternative linear conversion grade scale 40%	No	60
Assignment: Assignments involving use of e.g. statistical software, oral presentations, report writing.	Throughout the Trimester	n/a	Alternative linear conversion grade scale 40%	No	40

 

Resit In	Terminal Exam
Summer	Yes - 2 Hour

• Group/class feedback, post-assessment

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
Mr Ganesh Babu	Tutor
Elia Dufosse	Tutor
Sharath Madhu Manjunath	Tutor
Koyel Majumdar	Tutor
Brian O'Sullivan	Tutor

Spring
Lecture	Offering 1	Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33	Mon 10:00 - 10:50
Lecture	Offering 1	Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33	Wed 16:00 - 16:50
Computer Aided Lab	Offering 2	Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33	Mon 16:00 - 16:50
Computer Aided Lab	Offering 3	Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33	Mon 14:00 - 14:50
Computer Aided Lab	Offering 4	Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33	Thurs 12:00 - 12:50
Spring