# STAT40740 Multivariate Analysis (Online)

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.
Clustering
Classification
Multidimensional scaling
Principal components analysis
Factor analysis

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

25

Autonomous Student Learning

60

Online Learning

35

Total

120

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
Incompatibles:
STAT40150 - Multivariate Analysis, STAT40340 - Multivariate Analysis

Assessment Strategy
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: Written exam 2 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No

60

Continuous Assessment: Online assessments Throughout the Trimester n/a Alternative linear 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.
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