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:
STAT20230 - Modern Regression Analysis, STAT20240 - Predictive Analytics, STAT30240 - Predictive Analytics I

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


 

Assessment Strategy  

Description	Timing		Component Scale		% of Final Grade
Examination: 2 hour end of semester examination	2 hour End of Trimester Exam	No	Graded	No	60
Continuous Assessment: Assignments will be a mix of theory and computer based problem sheets.	Throughout the Trimester	n/a	Graded	No	40

 

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

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