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Mathematical Statistics (MAST90082)
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 1
Overview
Availability | Semester 1 |
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Fees | Look up fees |
The theory of statistical inference is important for applied statistics and as a discipline in its own right. After reviewing random samples and related probability techniques including inequalities and convergence concepts the theory of statistical inference is developed. The principles of data reduction are discussed and related to model development. Methods of finding estimators are given, with an emphasis on multi-parameter models, along with the theory of hypothesis testing and interval estimation. Both finite and large sample properties of estimators are considered. Applications may include robust and distribution free methods, quasi-likelihood and generalized estimating equations. It is expected that students completing this course will have the tools to be able to develop inference procedures in novel settings.
Intended learning outcomes
After completing this subject students should gain:
- a deeper understanding of the principles of mathematical statistics 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
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20005 | Statistics | Semester 2 (On Campus - Parkville) |
12.5 |
And any third-year subject in statistics or stochastic processes.
These can include the following subjects:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30001 | Stochastic Modelling | Semester 2 (On Campus - Parkville) |
12.5 |
MAST30025 | Linear Statistical Models | Semester 1 (On Campus - Parkville) |
12.5 |
MAST30027 | Modern Applied Statistics | Semester 2 (On Campus - Parkville) |
12.5 |
MAST30020 | Probability for Inference | Semester 1 (On Campus - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
None
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 1
Principal coordinator Richard Huggins Mode of delivery On Campus (Parkville) Contact hours Contact Hours: 36 hours comprising 2 one-hour lectures per week and 1 one-hour practice class per week. Total time commitment 170 hours Teaching period 27 February 2017 to 28 May 2017 Last self-enrol date 10 March 2017 Census date 31 March 2017 Last date to withdraw without fail 5 May 2017 Assessment period ends 23 June 2017 Semester 1 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 Data Science Course Doctor of Philosophy - Engineering Course Master of Commerce (Actuarial Science) Course Master 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