Methods of Mathematical Statistics (MAST90105)

Graduate courseworkPoints: 25On Campus (Parkville)

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Overview

Availability
Semester 1
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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: 4 March 2025

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 No longer available
MAST10021 Calculus 2: Advanced No longer available

AND

One of

Code Name Teaching period Credit Points
MAST10007 Linear Algebra No longer available
MAST10022 Linear Algebra: Advanced No longer available
MAST10010 Data Analysis No longer available
MAST10011 Experimental Design and Data Analysis No longer available

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 No longer available
MAST20005 Statistics No longer available
MAST20006 Probability for Statistics No longer available

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

DescriptionTimingPercentage

Four written assignments amounting to a total of up to 100 pages, due during regular intervals

  • 100 pages
During the teaching period20%

Written examination

  • 3 hours
Week 735%

Computer laboratory test

  • 45 minutes
End of the teaching period10%

Written examination

  • 3 hours
During the examination period35%

Last updated: 4 March 2025

Dates & times

  • Semester 1
    Principal coordinatorPavel Krupskiy
    Mode of deliveryOn Campus (Parkville)
    Contact hours4 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 commitment340 hours
    Teaching period 3 March 2025 to 1 June 2025
    Last self-enrol date14 March 2025
    Census date31 March 2025
    Last date to withdraw without fail 9 May 2025
    Assessment period ends27 June 2025

    Semester 1 contact information

    pavel.krupskiy@unimelb.edu.au

Time commitment details

340 hours

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

    Prescribed texts

    Hogg and Tanis, Probability and Statistical Inference. Prentice Hall.

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster 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.

    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

    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: 4 March 2025