# STAT40150 Multivariate Analysis

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).

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Curricular information is subject to change

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
Specified Learning Activities

32

Autonomous Student Learning

48

Lectures

24

Computer Aided Lab

11

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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Exam (In-person): 2hr in-person written exam. n/a Standard conversion grade scale 40% No

60

Assignment(Including Essay): Data analysis project n/a Standard conversion grade scale 40% No

40

Carry forward of passed components
No

Resit In Terminal Exam
Summer Yes - 2 Hour
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
Mr Ganesh Babu Tutor
Ms Laura Craig Tutor
Mr Brian Hassett Tutor
Koyel Majumdar Tutor