STAT30250

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 Hours:

Approaches to Teaching and Learning:

Lectures, labs covering materials implementation in R, and tutorials.

Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles

Pre-requisite:
STAT20230 - Modern Regression Analysis, STAT20240 - Predictive Analytics

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

Additional Information:
Students must have passed one of the listed pre-requisites.


 

Assessment Strategy

 

Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

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

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.