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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 | ||
---|---|---|---|---|---|
Continuous Assessment: Assignments will be a mix of theory and computer based problem sheets. | Throughout the Trimester | n/a | Graded | No | 40 |
Examination: 2 hour end of semester examination | 2 hour End of Trimester Exam | No | Graded | No | 60 |
Resit In | Terminal Exam |
---|---|
Summer | Yes - 2 Hour |
• Group/class feedback, post-assessment
The Assignments have class feedback posted on Brightspace or discussed in class.
Name | Role |
---|---|
Ms Mittal Mittal | Tutor |