Course Title: Multivariate Analysis Techniques

Part A: Course Overview

Course Title: Multivariate Analysis Techniques

Credit Points: 12.00


Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1309

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 1 2014,
Sem 1 2016

Course Coordinator: Dr. Yan Wang

Course Coordinator Phone: +61 3 9925 2381

Course Coordinator Email: yan.wang@rmit.edu.au

Course Coordinator Location: 8.9.34


Pre-requisite Courses and Assumed Knowledge and Capabilities

This course builds on a good knowledge of matrix algebra and univariate statistical inference (probability distribution, estimation procedures and statistical hypotheses testing).


Course Description

 

Multivariate analysis skills have been commonly recognized as part of the key requisites for analytics analysts. The complexity of most phenomena in the real world requires an investigator to collect and analyze observations on many different variables instead of a single variable. The desire for statistical techniques to elicit information from multivariate dimensional data thus becomes essential and crucial for data analysts.

The objective of the course is to introduce several useful multivariate techniques, making strong use of illustrative examples and a minimum of mathematics. The course will start with the extensions of univariate techniques to multivariate framework, such as multivariate normal distribution, confidence ellipse estimation, hypothesis testing, simultaneous confidence intervals and Bonferroni confidence intervals.  The course will also cover the techniques unique to the multivariate setting such as principal component analysis, factor analysis, discrimination, classification and clustering analysis.

Skills will be developed with SAS, a leading statistical analysis software package used in industry.


Objectives/Learning Outcomes/Capability Development

 

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical knowledge, methodologies and modern computational tools.

Problem-solving

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the relationship between the purpose of a model and the appropriate level of complexity and accuracy.

Communication

  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.


 

On completion of this course you should be able to:

  1. Perform exploratory analysis of multivariate data, such as plot multivariate data, calculating descriptive statistics, testing for multivariate normality; 
  2. Conduct statistical inference about multivariate means including hypothesis testing, confidence ellipsoid calculation and different types of confidence intervals estimation;
  3. Undertake statistical analyses using appropriate multivariate techniques, which include principal component, factor analysis, discriminant and clustering analysis;
  4. Analyse multivariate data using the SAS statistical software package.


Overview of Learning Activities

 

The course will be delivered through a combination of face-to-face lectures and computer lab practice. Lectures will explain theories underlying multivariate techniques, with demonstrations using real applications from varied disciplines. The course material is designed to offer you a balance between theory and applied examples. In addition the lab practice will provide students opportunities to learn programming for multivariate data analysis using the industry standard package SAS.

All course materials such as lecture notes, lab practice, exercises and assessment can be accessed from Blackboard LMS.

You are expected to undertake 2 hours of face-to-face lecture and lab sessions each week. Meanwhile it is recommended that an average of 4-6 hours/week of independent study is required for course review and assessment completion.


Overview of Learning Resources

 

There will be a prescribed textbook for this course. A list of recommended textbooks for this course is provided on Blackboard. All course materials, including lecture notes, lab exercises, practical exercises, assignments will be posted on Blackboard LMS.

The statistical package SAS can be accessed from the school computer labs, meanwhile students can get access to SAS through the RMIT MyDesktop system anywhere anytime.

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats


Overview of Assessment

This course has no hurdle requirements.

Assessment Tasks:

Assessment Task 1: Assignments
Weighting 20%
This assessment task supports CLOs 1, 2, 3, 4

Assessment Task 2: Tests
Weighting 30%
This assessment task supports CLOs 1, 2, 3, 4

Assessment 3: Examination
Weighting 50% 
This assessment supports CLOs 2 & 3