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Advanced Statistical Modelling (MAST90111)
Graduate courseworkPoints: 12.5Not available in 2021
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.
Overview
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Complex data consisting of dependent measurements collected at different times and locations are increasingly important in a wide range of disciplines, including environmental sciences, biomedical sciences, engineering and economics. This subject will introduce you to advanced statistical methods and probability models that have been developed to address complex data structures, such as functional data, geo-statistical data, lattice data, and point process data. A unifying theme of this subject will be the development of inference, classification and prediction methods able to cope with the dependencies that often arise in these data.
Intended learning outcomes
After completing this subject students should gain:
- An appreciation of the range and utility of advanced statistical models and a sound knowledge of their analysis using modern statistical methods.
- An appreciation of the computational methods required to fit these models and the ability to interpret the results of an analysis.
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;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments;
- computer skills: the ability to use statistical computing packages.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90082 | Mathematical Statistics | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
AND
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30025 | Linear Statistical Models | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
MAST90104 | A First Course In Statistical Learning | Semester 2 (Dual-Delivery - Parkville) |
25 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Any of the following:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90085 | Multivariate Statistical Techniques | Not available in 2024 |
12.5 |
MAST90083 | Computational Statistics & Data Science | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
MAST90084 | Statistical Modelling | 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: 3 November 2022
Assessment
Description | Timing | Percentage |
---|---|---|
Two written assignments amounting up to 50 pages, mid and late semester
| Second half of the teaching period | 20% |
Written examination
| During the examination period | 60% |
Project
| End of the teaching period | 20% |
Last updated: 3 November 2022
Dates & times
Not available in 2021
Time commitment details
170 hours
Last updated: 3 November 2022
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 Master of Science (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: 3 November 2022