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Multivariate Statistical Techniques (MAST90085)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Overview
| Availability | Semester 2 |
|---|---|
| Fees | Look up fees |
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: 3 November 2022
Eligibility and requirements
Prerequisites
1.
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90082 | Mathematical Statistics | Semester 1 (On Campus - Parkville) |
12.5 |
(may be taken concurrently)
And
2.
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30025 | Linear Statistical Models | Semester 1 (On Campus - Parkville) |
12.5 |
Or both of:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST90104 | A First Course In Statistical Learning | Semester 2 (On Campus - Parkville) |
25 |
| MAST90105 | Methods of Mathematical Statistics | Semester 1 (On Campus - Parkville) |
25 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30027 | Modern Applied Statistics | Semester 2 (On Campus - 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: 3 November 2022
Assessment
Additional details
- Up to 40 pages of written assignments (two assignments worth 10% each) due mid and late semester (20%)
- Three hour written examination (80%)
Last updated: 3 November 2022
Dates & times
- Semester 2
Coordinator Tingjin Chu Mode of delivery On Campus (Parkville) Contact hours Contact Hours: 5 one hour lectures and a one hour computer lab each fortnight Total time commitment 170 hours Teaching period 23 July 2018 to 21 October 2018 Last self-enrol date 3 August 2018 Census date 31 August 2018 Last date to withdraw without fail 21 September 2018 Assessment period ends 16 November 2018 Semester 2 contact information
Time commitment details
Estimated Total Time Commitment - 170 hours
Last updated: 3 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:
Type Name Course Master of Science (Mathematics and Statistics) Course Master of Philosophy - Engineering Course Master of Data Science Course Doctor of Philosophy - Engineering Course Ph.D.- Engineering Informal specialisation Mathematics 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.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Last updated: 3 November 2022