STAT20240 Predictive Analytics

Academic Year 2023/2024

The course is intended to be a (non-exhaustive) survey of regression techniques with an emphasis on implementation and application to real-world problems with accessible math content. Time permitting, the methods we will study include:
1. Exploratory data analysis
2. Simple Linear Regression (properties of least squares; t-test; F-test; R-squared; Confidence and Prediction Intervals)
3. Multiple Linear Regression (properties of least squares; t-test; F-test; R-squared; Confidence and Prediction Intervals)
4. Regression with Categorical Variables
5. Regression with Interaction terms
6. Polynomial Regression
7. Model Selection for Multiple Linear Models
8. Regression Diagnostics

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Curricular information is subject to change

Learning Outcomes:

By the end of the course, students should be able to:
Enter tabular data using R.
Plot data using R, to help in exploratory data analysis.
Formulate regression models for the data, while understanding some of the limitations and assumptions implicit in using these models.
Fit models using R and interpret the output.
Test for associations in a given model.
Use diagnostic plots and tests to assess the adequacy of a particular model.
Find confidence intervals for the effects of different explanatory variables in the model.
Use some basic model selection procedures, as found in R, to find a best model in a class of models.
Fit simple ANOVA models in R, treating them as special cases of multiple regression models.

Student Effort Hours: 
Student Effort Type Hours
Autonomous Student Learning

65

Lectures

24

Tutorial

10

Computer Aided Lab

11

Total

110

Approaches to Teaching and Learning:
2 Lectures a week one face-to-face and one online
1 tutorial a week from week 2 onwards
1 lab a week from week 2 onwards 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment: Assignment: Given a data set you must:
1. Analyse the data set using R.
2. Detail and interpret your results by answering a series of questions about your regression model.
Week 7 n/a Alternative linear conversion grade scale 40% No

30

No
Examination: Examination 2 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No

70

No

Carry forward of passed components
No
 
Resit In Terminal Exam
Spring 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?

Not yet recorded.

All books are available in the library:
1. Applied Regression Analysis and Generalized Linear Models by John Fox
2. Linear Models with R by J.Faraway
3. An R Companion to Linear Statistical Models by Christopher Hay-Jahans
Name Role
Dr Wagner Barreto-Souza Lecturer / Co-Lecturer
Dr Luiza Piancastelli Lecturer / Co-Lecturer
Ms Courtney Clarke Tutor
Kseniia Maksimova Tutor
Mr Matt Nagle Tutor