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STAT30250

Academic Year 2024/2025

Advanced Predictive Analytics (STAT30250)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Dr Wagner Barreto-Souza
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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.

About this Module

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:
Student Effort Type Hours
Autonomous Student Learning

72

Lectures

24

Tutorial

10

Laboratories

10

Total

116


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
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Final Exam End of trimester
Duration:
2 hr(s)
Alternative linear conversion grade scale 40% No
60
No
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

Carry forward of passed components
No
 

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
Catherine Higgins Tutor
Ms Catherine Higgins Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 10:00 - 10:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 10:00 - 10:50
Spring Tutorial Offering 1 Week(s) - 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 11:00 - 11:50
Spring Tutorial Offering 2 Week(s) - 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Fri 13:00 - 13:50
Spring Computer Aided Lab Offering 1 Week(s) - 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 13:00 - 13:50
Spring Computer Aided Lab Offering 2 Week(s) - 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 17:00 - 17:50