Modern Applied Statistics

This subject has the following teaching availabilities in 2016:

Estimated total time commitment of 170 hours

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Students who gain credit for both ACTL30001 Actuarial Modelling 1 and ACTL30004 Actuarial Statistics cannot also gain credit for MAST30027 Modern Applied Statistics.

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry.
The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website: http://www.services.unimelb.edu.au/disability/

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.

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.

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%).

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• 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.

This subject potentially can be taken as a breadth subject component for the following courses:

• Bachelor of Commerce
• Bachelor of Environments
• Bachelor of Music

You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.

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.

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.