Curricular information is subject to change.
By the end of the module students should be able to:
- Identify and fit a wide range of statistical models to data
- Identify important features influencing a given response variable
- Perform inference and computer uncertainty intervals for advanced predictive statistical models
- Use the statistical programmes R for generalised linear models, and generalized additive models
| Student Effort Type | Hours |
|---|---|
| Lectures | 24 |
| Tutorial | 10 |
| Computer Aided Lab | 10 |
| Autonomous Student Learning | 70 |
| Total | 114 |
Students must have completed STAT40790 Predictive Analytics (online)
| Description | Timing | Component Scale | % of Final Grade | |||
|---|---|---|---|---|---|---|
| Multiple Choice Questionnaire: MCQ on material covered in weeks 1 and 2 | Week 3 | n/a | Alternative linear conversion grade scale 40% | No | 10 |
No |
| Multiple Choice Questionnaire: MCQ on material covered in weeks 3 and 4 | Week 5 | n/a | Alternative linear conversion grade scale 40% | No | 10 |
No |
| Assignment: Project 2: Generalized Additive Models and Mixed Effects Models | Week 12 | n/a | Alternative linear conversion grade scale 40% | Yes | 40 |
Yes |
| Assignment: Project 1: Generalized Linear Models |
Week 9 | n/a | Alternative linear conversion grade scale 40% | Yes | 40 |
Yes |
| Resit In | Terminal Exam |
|---|---|
| Autumn | Yes - 2 Hour |
• Group/class feedback, post-assessment
The Assignments have class feedback posted on Brightspace or discussed in class.