1. Handbook
  2. Subjects
  3. Probability for Statistics
  4. Print

Probability for Statistics (MAST20006)

Undergraduate level 2Points: 12.5Dual-Delivery (Parkville)

You’re viewing the 2021 Handbook:
Or view archived Handbooks
In 2021, there will be three delivery modes for your subjects – Dual-Delivery, Online and On Campus.
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.
You’re currently viewing the 2021 version of this subject

Overview

Availability
Semester 1 - Dual-Delivery
FeesLook up fees

This subject develops the probability theory that is necessary to understand statistical inference. Properties of probability are reviewed, random variables are introduced, and their properties are developed and illustrated through common univariate probability models. Models for the joint behaviour of random variables are introduced, along with conditional probability and Markov chains. Methods for obtaining the distributions of functions of random variables are considered along with techniques to obtain the exact and approximate distributions of sums of random variables. These methods will be illustrated through some well known normal approximations to discrete distributions and by obtaining the exact and approximate distributions of some commonly used statistics. Computer packages are used for numerical and theoretical calculations but no programming skills are required.

Intended learning outcomes

At the completion of the subject, students are expected to:



  • 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.

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.
  • Become familiar with statistical computing packages.

Last updated: 22 November 2023

Eligibility and requirements

Prerequisites

One of

Code Name Teaching period Credit Points
MAST10006 Calculus 2
Summer Term (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
 12.5
MAST10009 Accelerated Mathematics 2
Semester 2 (Dual-Delivery - Parkville)
 12.5
MAST10021 Calculus 2: Advanced
Semester 2 (Dual-Delivery - Parkville)
 12.5

MAST10019 Calculus Extension Studies

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
MAST10008 Accelerated Mathematics 1
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 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
 12.5
MAST10022 Linear Algebra: Advanced
Semester 1 (Dual-Delivery - Parkville)
 12.5

MAST10018 Linear Algebra Extension Studies

MAST10013 UMEP Maths for High Achieving Students

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

MAST30015 Statistics for Mechanical Engineers (prior to 2011)

ELEN30002 Stochastic Signals and Systems (prior to 2011)

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: 22 November 2023

Assessment

DescriptionTimingPercentage

Five written assignments due at regular intervals amounting to a total of up to 50 pages

  • 50 pages
During the teaching period20%

A computer laboratory test

  • 45 minutes
End of semester10%

A written examination

  • 3 hours
During the examination period70%

Last updated: 22 November 2023

Dates & times

  • Semester 1
    Principal coordinatorSophie Hautphenne
    Mode of deliveryDual-Delivery (Parkville)
    Contact hours3 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 commitment170 hours
    Teaching period 1 March 2021 to 30 May 2021
    Last self-enrol date12 March 2021
    Census date31 March 2021
    Last date to withdraw without fail 7 May 2021
    Assessment period ends25 June 2021

    Semester 1 contact information

    sophiemh@unimelb.edu.au

Time commitment details

Estimated total time commitment of 170 hours

Last updated: 22 November 2023

Further information

Last updated: 22 November 2023