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Statistical Modelling (MAST90084)
Graduate courseworkPoints: 12.5On Campus (Parkville)
To learn more, visit 2023 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 |
---|---|
Fees | Look up fees |
Statistical models are central to applications of statistics and their development motivates new statistical theories and methodologies. Commencing with a review of linear and generalized linear models, analysis of variance and experimental design, the theory of linear mixed models is developed and model selection techniques are introduced. Approaches to non and semiparametric inference, including generalized additive models, are considered. Specific applications may include longitudinal data, survival analysis and time series modelling.
Intended learning outcomes
Upon completion of this subject, students should be able to:
- Explain the principles of statistical modelling;
- Apply the learned modelling techniques to real data arising from important applications;
- Relate the learned modelling techniques to other areas of studies in statistics.
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;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments
Last updated: 30 January 2024
Eligibility and requirements
Prerequisites
Students must meet one of the following prerequisite options:
Option 1
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30025 | Linear Statistical Models | Semester 1 (On Campus - Parkville) |
12.5 |
AND
Note: the following subject/s can also be taken concurrently (at the same time)
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90082 | Mathematical Statistics | Semester 1 (On Campus - Parkville) |
12.5 |
Option 2
All of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90104 | A First Course In Statistical Learning | Semester 2 (On Campus - Parkville) |
25 |
MAST90105 | Methods of Mathematical Statistics | Semester 1 (On Campus - Parkville) |
25 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30027 | Modern Applied Statistics | Semester 2 (On Campus - 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: 30 January 2024
Assessment
Description | Timing | Percentage |
---|---|---|
Four written assignments, up to a total of 50 pages (each worth 10% and approx. 10 hours in time committment) due in weeks 3, 6, 9, 12.
| Throughout the teaching period | 40% |
Written examination
| During the examination period | 60% |
Last updated: 30 January 2024
Dates & times
- Semester 1
Principal coordinator Dennis Leung Mode of delivery On Campus (Parkville) Contact hours 36 contact hours: 1 xtwo-hour interactive lecture per week and 1 x one-hour interactive lecture per week Total time commitment 170 hours Teaching period 27 February 2023 to 28 May 2023 Last self-enrol date 10 March 2023 Census date 31 March 2023 Last date to withdraw without fail 5 May 2023 Assessment period ends 23 June 2023 Semester 1 contact information
Time commitment details
Estimated Total Time Commitment - 170 hours
Last updated: 30 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 Data Science Course Ph.D.- Engineering Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Course Master of Science (Mathematics and Statistics) 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: 30 January 2024