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Mathematical Statistics (MAST90082)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
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 - Dual-Delivery |
---|---|
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: 31 January 2024
Eligibility and requirements
Prerequisites
Students must meet one of the following prerequisite options:
Option 1
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20005 | Statistics |
Summer Term (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - 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 (Dual-Delivery - Parkville) |
12.5 |
MAST30025 | Linear Statistical Models | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
MAST30027 | Modern Applied Statistics | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
MAST30020 | Probability for Inference | Semester 1 (Dual-Delivery - 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: 31 January 2024
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: 31 January 2024
Dates & times
- Semester 1
Principal coordinator Liuhua Peng Mode of delivery Dual-Delivery (Parkville) Contact hours 3 x one-hour interactive lectures per week Total time commitment 170 hours Teaching period 1 March 2021 to 30 May 2021 Last self-enrol date 12 March 2021 Census date 31 March 2021 Last date to withdraw without fail 7 May 2021 Assessment period ends 25 June 2021 Semester 1 contact information
Last updated: 31 January 2024
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 Doctor of Philosophy - Engineering Course Master of Commerce (Actuarial Science) Course Master of Data Science Course Ph.D.- Engineering Course Master of Philosophy - 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.
- 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: 31 January 2024