Learning Outcomes:

- 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

Learning Requirements:

Students must have completed STAT30240 Predictive Analytics

Pre-requisite:
STAT30240 - Predictive Analytics I

Incompatibles:
FIN30520 - Machine Learning Finance, STAT40770 - Adv Pred Analytics (online)


 

Assessment Strategy  

Description	Timing		Component Scale		% of Final Grade
Assignment: Project 4: Generalized Additive Models and Mixed Effects Models	Week 12	n/a	Graded	Yes	40
Multiple Choice Questionnaire: MCQ on material covered in weeks 3 and 4	Week 5	n/a	Graded	No	10
Multiple Choice Questionnaire: MCQ on the first two weeks of material	Week 3	n/a	Graded	No	10
Assignment: Project 1: Generalized Linear Models	Week 9	n/a	Graded	Yes	40

 

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.

1. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models.
2. Applied regression analysis and generalized linear models by Fox, John
3. An R companion to applied regression by Fox, John; Weisberg, Sanford
4. Semiparametric regression with R by Jaroslaw Harezlak, David Ruppert, and Matt P. Wand
5. Generalized Additive Models: An Introduction with R by Simon Wood.

Name	Role
Mr Shubbham Gupta	Tutor
Catherine Higgins	Tutor
Ms Catherine Higgins	Tutor
Uche Mbaka	Tutor