Learning Outcomes:

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

Indicative Module Content:

Student Effort Type	Hours
Lectures	24
Tutorial	10
Computer Aided Lab	10
Autonomous Student Learning	70
Total	114

Learning Requirements:

Students must have completed STAT40790 Predictive Analytics (online)

Pre-requisite:
STAT40790 - Predictive Analytics I (online

Incompatibles:
FIN30520 - Machine Learning Finance, STAT30250 - Advanced Predictive Analytics


 

Assessment Strategy  

Description	Timing	Component Scale		% of Final Grade
Assignment(Including Essay): Assignment 1: theoretical and computational questions.	Week 6, Week 7	Alternative linear conversion grade scale 40%	No	20	No
Assignment(Including Essay): Assignment 2: theoretical and computational questions.	Week 11, Week 12	Alternative linear conversion grade scale 40%	No	20	No
Exam (Online): Final Exam	End of trimester Duration: 2 hr(s)	Alternative linear conversion grade scale 40%	No	60	No

 

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.

1. Foundations of Linear and Generalized Linear Models by Alan Agresti
2. Applied Regression Analysis and Generalized Linear Models by John Fox
3. An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
4. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models by Julian J. Faraway
5. Regression Analysis of Count Data by A. Colin Cameron and Pravin K. Trivedi
6. Generalized Additive Models: An Introduction with R by Simon Wood