Statistical Decision Making
Duration
- One study period
Contact hours
- 36
On-campus unit delivery combines face-to-face and digital learning. For Online unit delivery, learning is conducted exclusively online.
Prerequisites
HMS796Z - Using R for Statistical Analysis
HMS796 - Using R for Statistical Analysis
Aims and objectives
1. Identify appropriate probability distributions for modelling chance occurrences.
2. Simulate such distributions and obtain estimates of distribution parameters using the method of moments and maximum likelihood estimation.
3. Navigate probabilistic methods for working with uncertainty.
4. Formulate probabilistic models for risk in real world contexts.
5. Use probabilistic models to design statistical systems for the management of risk and uncertainty.
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
This unit involves up to 150 hours of work including:
Hawthorn On Campus
| Type | Hours per week | Number of Weeks | Total |
| Face to Face Contact Seminar | 3 | 12 | 36 |
| Specified Learning Activities Discussion boards, watching lecture recording, readings, quizzes. | 2 | 12 | 24 |
| Unspecified Learning Activities Independent study, assignment preparation, revision | 7.5 | 12 | 90 |
| TOTAL |
|
| 150 hours/12.5cp |
Hawthorn Online
| Type | Hours per week | Number of Weeks | Total |
| Online Specified Learning Activities Discussion boards, watching lecture recording, readings, quizzes. Independent study, Assignment preparation, Revision | 12.5 | 12 | 150 |
| TOTAL |
|
| 150 hours/12.5cp |
Assessment
| Types | Individual or Group task | Weighting | Assesses attainment of these ULOs |
| Examination | Individual | 50% | 1,4,5 |
| Online Quizzes (10) | Individual | 10% | 1, 5 |
| *Assignments (2) | Individual | 40% (20% each) | 2,3,4 |
Content
- 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, Inventory Control, Reliability Theory, Markov Processes)
Study resources
- Reading materials and text books.