Mathematical Statistics (MAST90082)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
| Availability | Semester 1 |
|---|---|
| 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:
- Understand the principles of mathematical statistics and some of its important applications; and
- Be able 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; and
- Time-management skills: the ability to meet regular deadlines while balancing competing commitments
Last updated: 4 March 2025
Eligibility and requirements
Prerequisites
Students must meet one of the following prerequisite options:
Option 1
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST20005 | Statistics |
Semester 2 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
|
12.5 |
AND
12.5 credit points of level 3 subject in statistics or stochastic processes which could include one of the below:
| 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 |
Option 2
Admission into the MC-DATASC Master of Data Science
AND
MAST90104 A First Course In Statistical Learning
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: 4 March 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
Continuing assessment of up to 40 hours work, worth 20% of the mark, throughout the semester
| Throughout the teaching period | 20% |
Written examination
| During the examination period | 80% |
Last updated: 4 March 2025
Dates & times
- Semester 1
Principal coordinator Liuhua Peng Mode of delivery On Campus (Parkville) Contact hours 3 x one-hour interactive lectures per week Total time commitment 170 hours Teaching period 3 March 2025 to 1 June 2025 Last self-enrol date 14 March 2025 Census date 31 March 2025 Last date to withdraw without fail 9 May 2025 Assessment period ends 27 June 2025 Semester 1 contact information
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 4 March 2025
Further information
- Texts
- Related Handbook entries
This subject contributes to the following:
Type Name Course Ph.D.- Engineering Course Master of Commerce (Actuarial Science) Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Course Master of Science (Mathematics and Statistics) Course Master of Data Science 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.
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
Last updated: 4 March 2025