Statistical Decision Making
PostgraduateSWI-STA800062019
Course information for 2019 intake
Get objective about basic data types and syntax in R software. Equate linear probability distributions and apply GLM modelling. Survey population traits and choose values to maximise probabilities. Model study on maximum likelihood estimation.
Enrolments for this course are closed, but you may have other options to start studying now. Book a consultation to learn more.
- Study method
- 100% online
- Assessments
- Subject may require attendance
- Entry requirements
- Part of a degree
- Duration
- -
- Loan available
- FEE-HELP available
Statistical Decision Making
About this subject
Students who successfully complete this subject will be able to:
- Identify appropriate probability distributions for modelling chance occurrences
- Simulate such distributions and obtain estimates of distribution parameters using the method of moments and maximum likelihood estimation
- Navigate probabilistic methods for working with uncertainty
- Formulate probabilistic models for risk in real world contexts
- Use probabilistic models to design statistical systems for the management of risk and uncertainty.
- Introduction to commonly used probability distributions
- Introduction to Method of Moments and Maximum Likelihood Estimation (MLE) for estimation purposes
- Applications for discrete, continuous and mixture distributions
- Applications for univariate and multivariate distributions
- Development of statistical models for identifying risk factors
- Formulation of commonly used probabilistic models and systems for managing uncertainty (e.g. Acceptance Sampling, Process Control, Queuing Theory)
Please note: this subject was previously titled Using R for Statistical Analysis
Students will learn to model and manage uncertainty and risk. Some of the most commonly used probability distributions will be introduced together with Monte Carlo simulations. The concept of Maximum Likelihood Estimation (MLE) will also be introduced, allowing the estimation of distribution parameters. Probabilistic methods and models will then be illustrated using appropriate examples and software.
Please note: assessment values are indicative only, details will be advised at the start of the subject.
- Quizzes — Online (10%)
- Invigilated Exam (50%)
- Assignments — 2 (40%)
For textbook details check your university's handbook, website or learning management system (LMS).
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Entry requirements
To enrol in this subject, you must be admitted into a degree.
Prior study
You must have successfully completed the following subject(s) before starting this subject:
one of
- SWI-STA70002-Multivariate Statistics
SWI-HMS780 (Not currently available)
Equivalent subjects
You should not enrol in this subject if you have successfully completed any of the following subject(s) because they are considered academically equivalent:
SWI-HMS796 (Not currently available)
Additional requirements
- Other requirements -
You will require the R software package which is available freely on the web.
Study load
- 0.125 EFTSL
- This is in the range of 10 to 12 hours of study each week.
Equivalent full time study load (EFTSL) is one way to calculate your study load. One (1.0) EFTSL is equivalent to a full-time study load for one year.
Find out more information on Commonwealth Loans to understand what this means to your eligibility for financial support.