Offered By	ANU College of Business and Economics
Minimum	48 units
Academic Contact	ANU College of Business and Economics and ANU College of Business and Economics
Academic Plan	7404XMAST
CRICOS Code	029317G
UAC Code	832215(Master of Applied Statistics) 835215(Master of Applied Statistics)
Areas of Interest	Statistics and Statistics

The Master of Applied Statistics degree is designed to enhance the professional expertise of graduates engaged in work of a quantitative nature. This program is also designed for graduate students to extend their knowledge of statistics to an advanced academic level, with a view to possible PhD study.

Eligible domestic students undertaking the Master of Applied Statistics degree program  may be able to access Youth Allowance, Austudy and the Pensioner Education Supplement. For full details, please visit: http://www.dest.gov.au/sectors/career_development/programmes_funding/programme_categories/student_income_support/  

Commonwealth Supported Places

The ANU College of Business and Economics offers a limited number of Commonwealth Supported Places (CSP) for Australian students in graduate programs. For further information, please read the general information.

 Admission Requirements

1. A bachelors degree in statistics, econometrics or a related quantitative field from an Australian tertiary institution (or equivalent) with an average of 65% or better for the final two years of their degree program.
2. Background quantitative study equivalent to at least two years of university level statistical and mathematical study including calculus and linear algebra, as well as mathematical statistics and linear regression theory.
3. Otherwise qualified applicants who have not completed suitable background studies in statistics will be required to undertake preliminary work in order to be eligible for admission to the program.

Learning Outcomes

This degree has no compulsory courses, but students must complete at least 6 (of 8) courses from a prescribed list of core courses. Hence learning outcomes will vary between students, depending on course selection. The list below contains learning outcomes from most of these core courses, so all graduates would be anticipated to have developed some of the below skills.

• Develop statistical computing skills for use in quantitative and data-based problem solving
• Understand and be able to apply the processes and applications of design of experiments and surveys
• Briefly discuss the characteristics, advantages and disadvantages of several basic types of survey
• Understand in detail the mathematics behind several different sampling techniques
• Design and analyse the results of an appropriate survey under different conditions
• Understand the rile of generalized linear modeling techniques in modern applied statistics
• Appreciate the structural assumptions and diagnostics used in GLMs for both discrete and continuous responses
• Appreciate the use of both fixed and random effects in mixed GLMs
• Understand and be able to apply the principles of data representation, summarization and presentation with particular emphasis on the use of graphics
• Understand the use of multivariate statistical techniques and appreciate the application of these techniques in varied areas of research
• Understand and apply appropriately the notion of a parametric probability model and point estimation of the parameters of these models.
• Extend estimation procedures to include a measure of their accuracy and our confidence in them by examining the area of interval estimation
• Assess the plausibility of pre-specified ideas about the parameters of the model by examining the area of hypothesis testing
• Understand and appreciate the role of non-parametric statistics, wherein estimation and analysis techniques are developed that are not heavily dependent on the specification of an underlying parametric model
• Understand and be able to apply techniques such as conditioning and transformations, Markov chain techniques, continuous-time Markov processes, classical Markov chains in insurance, engineering and biology, Brownian motion and related processes
• Understand and apply the concept of stationarity to the analysis of time series data in various contexts
• Use the Box-Jenkins approach to model time series data empirically
• Run and interpret time series models involving dynamic volatility and/or trends
• Use multivariate time series models such as vector autoregression to analyse time series data and apply techniques such as impulse response analysis, Granger causality and variance decomposition to interpret results
• Analyse and interpret co-integrated data in various contexts using an error correction framework such as the Engle-Granger methodology and the vector error correction
• Develop fundamental research skills, such as data collection, data processing  and model estimation and interpretation, in applied time series

The information published on the Study at ANU 2013 website applies to the 2013 academic year only. All information provided on this website replaces the information contained in the Study at ANU 2012 website.