STAT40340 Multivariate Analysis

Academic Year 2019/2020

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 introduce and make use of the free statistical software package 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 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. A deeper level of understanding is expected from Master's students than undergraduates.

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

36
Autonomous Student Learning

80
Total

151
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 is essential. Students must also be familiar with elementary matrix algebra including eigenvalues and eigenvectors.


Module Requisites and Incompatibles
Incompatibles:
STAT40150 - Multivariate Analysis, STAT40740 - Multivariate Analysis (Online)


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Electronically submitted assignment that will consist of some practical analysis, written responses and/or mathematical derivations. Throughout the Trimester n/a Standard conversion grade scale 40% No

45
Assignment: Electronically submitted assignment that will consist of some practical analysis, written responses and/or mathematical derivations. Throughout the Trimester n/a Standard conversion grade scale 40% No

15
Assignment: Electronically submitted assignment that will consist of some practical analysis, written responses and/or mathematical derivations. Throughout the Trimester n/a Standard conversion grade scale 40% No

40

Carry forward of passed components
No
 
Resit In Terminal Exam
Autumn 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
Mr Xian Yao Gwee Tutor
Mr Sen Hu Tutor
Ms Sajal Kaur Minhas Tutor
John O'Sullivan Tutor
Ms Jinbo Zhao Tutor