Statistical Decision Making
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.
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- 24 Feb 2019
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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)
- Discussion forum/Discussion Board
- Online Quizzes/Tests
- Online assignment submission
- Podcasting/Lecture capture
- Standard Media
- Web links
- Welcome letter
- Printable format materials
You cannot enrol in this subject if you have successfully completed any of the following subject(s) because they are considered academically equivalent:
You must have successfully completed the following subject(s) before starting this subject:
SWI-STA70002-Multivariate Statistics , or SWI-HMS780
- OtherDetails -
You will require the R software package which is available freely on the web.
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%)