| Type: | Postgraduate Subject |
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| Code: | DTSC71-302 |
| EFTSL: | 0.125 |
| Faculty: | Bond Business School |
| Semesters offered: |
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| Credit: | 10 |
| Study areas: |
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| Subject fees: |
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Description
This subject covers the theory and practice of modern statistical learning, regression and classification modelling. Techniques covered range from traditional model selection and generalised linear model structures to modern, computer-intensive methods including generalised additive models, splines and tree methods. Methods to handle continuous, ordinal and nominal response variables and assessment of fit via cross-validation and residual diagnostics are also considered. All techniques will be investigated via practical application on real data using the statistical software package R.
Subject details
Learning outcomes
- Demonstrate knowledge of the limitations of linear regression models and the ability to develop an appropriate regression model given the circumstance.
- Critically evaluate and appropriately choose between applications of a variety of linear and generalised linear regression modelling structures, including regularisation, dimension reduction and sequential variable selection.
- Apply non-normal dependent variable regression and classification models, and critically discuss the differences in associated predictive and structural issues.
- Develop prediction models utilising splines, polynomials, recursive partitioning and general additive methods and critically compare and contrast the model results.
- Demonstrate ability to produce creative analytic solutions addressing a potentially multifaceted issue or problem.
- Demonstrate ability to verbally communicate the results of a statistical learning investigation to audiences of various levels of quantitative background.
Enrolment requirements
| Requisites: |
Nil |
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| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent):Possess demonstrable knowledge in the theory and application of simple and multiple linear regression models to subject levels, such as Linear Models and Applied Econometrics, as well as basic data science concepts and techniques to subject levels, such as Data Science. |
| Restrictions: |
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Subject outlines
- May 2024 [Standard - Statistical Learning and Regression Models]
- May 2023 [Standard - Statistical Learning and Regression Models]
- May 2022 [Standard - Statistical Learning and Regression Models]
- May 2021 [Standard - Statistical Learning and Regression Models]
- May 2020 [Standard - Statistical Learning and Regression Models]
Subject dates
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May 2024
Standard Offering Enrolment opens: 17/03/2024 Semester start: 13/05/2024 Subject start: 13/05/2024 Cancellation 1: 27/05/2024 Cancellation 2: 03/06/2024 Last enrolment: 26/05/2024 Withdraw - Financial: 08/06/2024 Withdraw - Academic: 29/06/2024 Teaching census: 07/06/2024
| Standard Offering | |
|---|---|
| Enrolment opens: | 17/03/2024 |
| Semester start: | 13/05/2024 |
| Subject start: | 13/05/2024 |
| Cancellation 1: | 27/05/2024 |
| Cancellation 2: | 03/06/2024 |
| Last enrolment: | 26/05/2024 |
| Withdraw - Financial: | 08/06/2024 |
| Withdraw - Academic: | 29/06/2024 |
| Teaching census: | 07/06/2024 |