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Modern Applied Statistics (MAST30027)
Undergraduate level 3Points: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Email: heejung.shim@unimelb.edu.au
Overview
Availability | Semester 2 |
---|---|
Fees | Look up fees |
Modern applied statistics combines the power of modern computing and theoretical statistics. This subject considers the computational techniques required for the practical implementation of statistical theory, and includes Bayes and Monte-Carlo methods. The subject focuses on the application of these techniques to generalised linear models, which are commonly used in the analysis of categorical data.
Intended learning outcomes
At the completion of the subject, students should:
- Understand the theory and applications of various mainstream applied statistical methods;
- Be able to use appropriate statistical methods to develop effective models or inferential procedures and provide sound interpretations for real-world data analysis;
- Be able to use a computer package to perform statistical computing and data 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 andexpress 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: 15 February 2024
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30025 | Linear Statistical Models | Semester 1 (On Campus - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
Students who gain credit for both ACTL30001 Actuarial Modelling 1 and ACTL30004 Actuarial Statistics cannot also gain credit for MAST30027 Modern Applied Statistics.
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: 15 February 2024
Assessment
Additional details
Six written assignments due at regular intervals during semester amounting to a total of up to 50 pages (30%), and a 3-hour written examination in the examination period (70%).
Last updated: 15 February 2024
Dates & times
- Semester 2
Principal coordinator Heejung Shim 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 24 July 2017 to 22 October 2017 Last self-enrol date 4 August 2017 Census date 31 August 2017 Last date to withdraw without fail 22 September 2017 Assessment period ends 17 November 2017 Semester 2 contact information
Email: heejung.shim@unimelb.edu.au
Time commitment details
Estimated total time commitment of 170 hours
Last updated: 15 February 2024
Further information
- Texts
Prescribed texts
Recommended texts and other resources
- Faraway, Extending the Linear Model, R. Chapman & Hall, 2006.
- McCullagh & Nelder, Generalised Linear Models, 2nd edition. Chapman & Hall, 1989.
- Jones, Maillardet & Robinson, Introduction to Scientific Programming and Simulation Using R, 2nd Edition, Taylor and Francis, 2014.
- Gelman, Carlin, Stern, Dunson, Vehtari & Rubin, Bayesian Data Analysis, 3rd Edition, CRC Press, 2014.
- Lunn, Jackson, Best, Thomas & Spiegelhalter, The BUGS Book: A Practical Introduction to Bayesian Analysis, CRC Press, 2013.
- Subject notes
This subject is available for science credit to students enrolled in the BSc (both pre-2008 and new degrees), BASc or a combined BSc course.
- Related Handbook entries
This subject contributes to the following:
Type Name Informal specialisation Statistics / Stochastic Processes Informal specialisation Science-credited subjects - new generation B-SCI and B-ENG. Informal specialisation Selective subjects for B-BMED Informal specialisation Statistics / Stochastic Processes Major Statistics / Stochastic Processes Informal specialisation Statistics / Stochastic Processes Major Data Science - Breadth options
This subject is available as breadth in the following courses:
- 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: 15 February 2024