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Curricular information is subject to change
- Ability to estimate model parameters, check model assumptions and modify a model as necessary.
- Ability to interpret parameter estimates and their standard errors.
- Ability to use remedial measures if model assumptions found to be invalid
- Ability to identify an appropriate statistical model for a specified investigation given the data collecting background.
- Ability to implement all of the above using statistical software.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Tutorial | 10 |
Laboratories | 10 |
Autonomous Student Learning | 72 |
Total | 116 |
Students must have completed STAT30240 Predictive Analytics
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Project 2: Generalized Linear Models |
Week 9 | n/a | Graded | Yes | 33 |
Assignment: Project 3: Generalized Additive Models and Mixed Effects Models | Week 12 | n/a | Graded | Yes | 33 |
Assignment: Project 1: Weighted Least Squares, Model Selection, Ridge Regression, Interactions, Polynomial Terms and Penalized Smoothing | Week 5 | n/a | Graded | Yes | 33 |
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.