| Type: | Undergraduate Subject |
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| Code: | DTSC13-307 |
| EFTSL: | 0.125 |
| Faculty: | Bond Business School |
| Credit: | 10 |
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Description
This subject extends the investigation of modern statistical modelling techniques initiated in Statistical Learning and Regression Models. Topics range from modern applied resampling/bootstrap methods to develop robust measures of model accuracy, to handling correlated data with mixed-effects models, and Bayesian Networks. Analysis will be conducted in programming languages such as R and Python.
Subject details
Learning outcomes
- Explain key aspects of data to assess the need for a particular model structure.
- Compare a variety of statistical model structures to choose the most appropriate.
- Apply resampling methods to develop robust measures of model accuracy.
- Interpret the results from a variety of statistical model types.
- Communicate the results of statistical models clearly and accurately to both technical and non-technical audiences.
Enrolment requirements
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Pre-requisites:Co-requisites:There are no co-requisites |
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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.
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