Quantitative Prediction
Overview
This unit takes a mathematical approach to equip students with the skills they need to perform complex analysis of datasets, be they scientific, industrial, financial or personal. This unit introduces students to the key mathematical methods used in formulating and solving such problems.
Requisites
02-November-2025
Learning outcomes
Students who successfully complete this unit will be able to:
- Examine how different types of analysis can be applied to sets of data to help quantify decision making in business, government, science and engineering.
- Solve problems through quantifying and understanding different classes of data.
- Use stochastic calculus and probability distributions to model data, analyse systems and predict outcomes, while evaluating the performance of these predictive models.
- Manipulate datasets and effect computation using software packages such as Matlab and R.
Teaching methods
Hawthorn
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
| On-campus Lecture | 3.00 | 12 weeks | 36 |
| On-campus Class | 1.00 | 12 weeks | 12 |
| On-campus Class | 1.00 | 6 weeks | 6 |
| Online Learning activities | 2.00 | 12 weeks | 24 |
| Unspecified Activities Independent Learning | 6.00 | 12 weeks | 72 |
| TOTAL | 150 |
Assessment
| Type | Task | Weighting | ULO's |
|---|---|---|---|
| Assignment 1 | Individual/Group | 10 - 20% | 1,2,3,4 |
| Assignment 2 | Individual/Group | 10 - 20% | 1,4 |
| Examination | Individual | 40 - 50% | 1,2,3,4 |
| Online Quizzes | Individual | 5 - 10% | 1,2,3,4 |
| Test 1 | Individual | 10 - 20% | 1,2 |
| Test 2 | Individual | 10 - 20% | 1,3 |
Hurdle
As the minimum requirements of assessment to pass a unit and meet all ULOs to a minimum standard, an undergraduate student must have achieved:
(i) an aggregate mark of 50% or more, and(ii) at least 40% in the final exam.Students who do not successfully achieve hurdle requirement (ii) will receive a maximum of 45% as the total mark for the unit.
Content
- Probability theory (random variabes, distributions and the central limit theorem)
- Analysis of time series and stochastic processes
- Modeling with autoregressive models
- Manipulation of datasets in statistical programming languages such as R and Matlab.
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.