STAT40770 Adv Pred Analytics (online)

Academic Year 2023/2024

Topics covered:

1. Review of Linear Regression
2. Regularised Regression
3. Generalised Linear Models
4. Quasi-Likelihood Models
5. Regression for Counts
6. Mixed Effects Models
7. Generalised Additive Models

All the material is supplemented with its implementation in the R programming language.

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Curricular information is subject to change

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 Hours: 
Student Effort Type Hours




Computer Aided Lab


Autonomous Student Learning




Approaches to Teaching and Learning:
Lectures, labs covering materials implementation in R, and assignments.
Requirements, Exclusions and Recommendations
Learning Requirements:

Students must have completed STAT40790 Predictive Analytics (online)

Module Requisites and Incompatibles
STAT40790 - Predictive Analytics I (online

FIN30520 - Machine Learning Finance, STAT30250 - Advanced Predictive Analytics

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Assignments will be a mix of theory and computer based problem sheets. Throughout the Trimester n/a Graded No


Examination: 2 hour end of semester examination 2 hour End of Trimester Exam No Graded No


Carry forward of passed components
Resit In Terminal Exam
Summer Yes - 2 Hour
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
Name Role
Ms Mittal Mittal Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.

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