Linear Statistical Models (MAST30025)
Undergraduate level 3Points: 12.5On Campus (Parkville)
Overview
| Availability | Semester 1 |
|---|---|
| Fees | Look up fees |
Linear models are central to the theory and practice of modern statistics. They are used to model a response as a linear combination of explanatory variables and are the most widely used statistical models in practice. Starting with examples from a range of application areas this subject develops an elegant unified theory that includes the estimation of model parameters, quadratic forms, hypothesis testing using analysis of variance, model selection, diagnostics on model assumptions, and prediction. Both full rank models and models that are not of full rank are considered. The theory is illustrated using common models and experimental designs.
Intended learning outcomes
On completion of this subject students should be able to
- Understand the underlying statistical theory of linear models and the limitations of such models;
- Fit linear models to data using a standard statistical computing package and interpret the results.
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: 21 January 2025
Eligibility and requirements
Prerequisites
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST20005 | Statistics | Semester 2 (On Campus - Parkville) |
12.5 |
Plus one of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST10007 | Linear Algebra |
Semester 1 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
| MAST10008 | Accelerated Mathematics 1 | Semester 1 (On Campus - Parkville) |
12.5 |
- MAST10013 UMEP Maths for High Achieving Students
Corequisites
None
Non-allowed subjects
Students may only gain credit for one of
- MAST30025 Linear Statistical Models
- 620-371 Linear Models (prior to 2010).
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: 21 January 2025
Assessment
Additional details
Two or three written assignments due at regular intervals during semester amounting to a total of up to 50 pages (20%), and a 3-hour written examination in the examination period (80%).
Last updated: 21 January 2025
Dates & times
- Semester 1
Principal coordinator Yao-Ban Chan Mode of delivery On Campus (Parkville) Contact hours 3 x one hour lectures per week, 1 x one hour computer laboratory class per week Total time commitment 170 hours Teaching period 26 February 2018 to 27 May 2018 Last self-enrol date 9 March 2018 Census date 31 March 2018 Last date to withdraw without fail 4 May 2018 Assessment period ends 22 June 2018 Semester 1 contact information
Email: yaoban@unimelb.edu.au
Time commitment details
Estimated total time commitment of 170 hours
Last updated: 21 January 2025
Further information
- Texts
- Subject notes
- Related Handbook entries
This subject contributes to the following:
Type Name Informal specialisation Environments Discipline subjects Major Data Science Informal specialisation Statistics / Stochastic Processes Major Environmental Science Major Statistics / Stochastic Processes Informal specialisation Selective subjects for B-BMED Informal specialisation Statistics / Stochastic Processes Informal specialisation Science-credited subjects - new generation B-SCI and B-ENG. Informal specialisation Statistics / Stochastic Processes specialisation Informal specialisation Operations Research / Discrete Mathematics specialisation - Breadth options
- 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
Last updated: 21 January 2025