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Methods of Mathematical Statistics (MAST90105)
Graduate courseworkPoints: 25Dual-Delivery (Parkville)
From 2023 most subjects will be taught on campus only with flexible options limited to a select number of postgraduate programs and individual subjects.
To learn more, visit COVID-19 course and subject delivery.
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 |
This subject introduces probability and the theory underlying modern statistical inference. Properties of probability are reviewed, univariate and multivariate random variables are introduced, and their properties are developed. It demonstrates that many commonly used statistical procedures arise as applications of a common theory. Both classical and Bayesian statistical methods are developed. Basic statistical concepts including maximum likelihood, sufficiency, unbiased estimation, confidence intervals, hypothesis testing and significance levels are discussed. Computer packages are used for numerical and theoretical calculations.
Intended learning outcomes
- Develop a systematic understanding of probability, random variables, probability distributions and probability models, and their relevance to statistical inference;
- Be able to formulate standard probability models from real world applications and critically assess them;
- Be able to apply the properties of probability distributions, moment generating functions, variable transformations and conditional expectations to analyse common random variables and probability models;
- Be able to use a computer package to perform algebraic and computational tasks in probability analyses.
- Be familiar with the basic ideas of estimation and hypothesis testing
- Be able to carry out many standard statistical procedures using a statistical computing package.
- Develop the ability to fit probability models to data by both estimating and testing hypotheses about model parameters.
Generic skills
In addition to learning specific skills that will assist students in their future careers in science, they should progressively acquire generic skills from this subject 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.
- computer skills: the ability to use statistical computing packages
Last updated: 31 January 2024
Eligibility and requirements
Prerequisites
Students must meet one of the following prerequisite options:
Option 1
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST10006 | Calculus 2 |
Semester 1 (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
Summer Term (Dual-Delivery - Parkville)
|
12.5 |
MAST10021 | Calculus 2: Advanced | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
AND
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST10007 | Linear Algebra |
Summer Term (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
|
12.5 |
MAST10022 | Linear Algebra: Advanced | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
MAST10010 | Data Analysis 1 | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
MAST10011 | Experimental Design and Data Analysis | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
Option 2
Admission into one of the following:
- MC-DATASC Master of Data Science
- GD-DATASC Graduate Diploma in Data Science
Corequisites
None
Non-allowed subjects
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20004 | Probability |
Semester 1 (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
MAST20005 | Statistics |
Summer Term (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
MAST20006 | Probability for Statistics | Semester 1 (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: 31 January 2024
Assessment
Description | Timing | Percentage |
---|---|---|
Four written assignments amounting to a total of up to 100 pages, due during regular intervals
| During the teaching period | 20% |
Written examination
| Week 7 | 35% |
Computer laboratory test
| End of the teaching period | 10% |
Written examination
| During the examination period | 35% |
Last updated: 31 January 2024
Dates & times
- Semester 1
Principal coordinator Pavel Krupskiy Mode of delivery Dual-Delivery (Parkville) Contact hours 4 x one hour lectures per week, 1 x one hour practice class per week, and 1 x one hour computer laboratory class per week Total time commitment 340 hours Teaching period 28 February 2022 to 29 May 2022 Last self-enrol date 11 March 2022 Census date 31 March 2022 Last date to withdraw without fail 6 May 2022 Assessment period ends 24 June 2022 Semester 1 contact information
Time commitment details
340 hours
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
Hogg and Tanis, Probability and Statistical Inference. Prentice Hall.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Data Science - 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