Multivariate Statistical Techniques (MAST90085)

Graduate courseworkPoints: 12.5Not available in 2022

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Overview

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Multivariate statistics concerns the analysis of collections of random variables that has general applications across the sciences and more recently in bioinformatics. It overlaps machine learning and data mining, and leads into functional data analysis. Here random vectors and matrices are introduced along with common multivariate distributions. Multivariate techniques for clustering, classification and data reduction are given. These include discriminant analysis and principal components. Classical multi-variate regression and analysis of variance methods are considered. These approaches are then extended to high dimensional data, such as that commonly encountered in bioinformatics, motivating the development of multiple hypothesis testing techniques. Finally, functional data is introduced.

Intended learning outcomes

After completing this subject students should gain:




 

  • a deeper understanding of the principles of statistical modelling and some of its important applications.
  • the ability to pursue further studies in this and related areas

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments

Last updated: 12 November 2022

Eligibility and requirements

Prerequisites

Students must meet one of the following prerequisite options:

Option 1

Code Name Teaching period Credit Points
MAST30025 Linear Statistical Models
Semester 1 (Dual-Delivery - Parkville)
 12.5

AND

Note: the following subject/s can also be taken concurrently (at the same time)

Code Name Teaching period Credit Points
MAST90082 Mathematical Statistics
Semester 1 (Dual-Delivery - Parkville)
 12.5

Option 2

All of

Code Name Teaching period Credit Points
MAST90104 A First Course In Statistical Learning
Semester 2 (Dual-Delivery - Parkville)
 25
MAST90105 Methods of Mathematical Statistics
Semester 1 (Dual-Delivery - Parkville)
 25

Corequisites

None

Non-allowed subjects

Code Name Teaching period Credit Points
MAST90138 Multivariate Statistics for Data Science
Semester 2 (Dual-Delivery - Parkville)
 12.5

Recommended background knowledge

Code Name Teaching period Credit Points
MAST30027 Modern Applied Statistics
Semester 2 (Dual-Delivery - Parkville)
 12.5

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 12 November 2022

Assessment

DescriptionTimingPercentage

Up to 40 pages of written assignments (two assignments worth 10% each) due mid and late semester

  • 40 pages
During the teaching period20%

Written examination

  • 3 hours
80%

Last updated: 12 November 2022

Dates & times

Not available in 2022

Time commitment details

Estimated Total Time Commitment - 170 hours

Last updated: 12 November 2022

Further information

  • Texts

    Prescribed texts

    There are no specifically prescribed or recommended texts for this subject.

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Data Science
    CoursePh.D.- Engineering
    CourseDoctor of Philosophy - Engineering
    CourseMaster of Philosophy - Engineering
    CourseMaster of Science (Mathematics and Statistics)
    Informal specialisationMathematics and Statistics
  • Available through the Community Access Program

    About the Community Access Program (CAP)

    This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

    Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants

    Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

  • Available to Study Abroad and/or Study Exchange Students

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 12 November 2022