# STAT40790 Predictive Analytics I (online

Topics covered:

1. Matrix revision and Exploratory data analysis;

2. Simple linear regression (SLR): properties of least squares estimates; t-tests; F-tests; Confidence intervals; Prediction intervals; Complete SLR analysis in the R statistical software;

3. Multiple linear regression (MLR): properties of least squares estimates; t-tests; F-tests; Confidence intervals; Prediction intervals; Complete MLR analysis in the R statistical software;

4. Categorical Predictors and Interactions

5. Analysis of Variance

6. Regression Diagnostics

7. Variable Selection and Model Building

All the material is supplemented with its implementation in the R programming language which is rated 7th in IEEE list of top programming languages. The module will use a variety of real-world examples to illustrate the use of regression models and cover both theoretical and practical considerations.

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Curricular information is subject to change

Learning Outcomes:

By the end of the module students should be able to:
(i) Interpret scatterplots for bivariate data.
(ii) Define the correlation coefficient for bivariate data.
(iii) Explain the interpretation of the correlation coefficient for bivariate data and perform statistical inference as appropriate.
(iv) Calculate the correlation coefficient for bivariate data.
(v) Explain what is meant by response and explanatory variables.
(vi) Derive the least squares estimates of the slope and intercept parameters in a simple linear regression model.
(vii) Perform statistical inference on the slope parameter.
(viii) Describe the use of measures of goodness of fit of a linear regression model.
(ix) Use a fitted linear relationship to predict a mean response or an individual response with confidence limits
(x) Use residuals to check the suitability and validity of a linear regression model.
(xi) State the multiple linear regression model (with several explanatory variables).
(xii) Use appropriate software to fit a multiple linear regression model to a data set and interpret the output.
(xiii) Use measures of model fit to select an appropriate set of explanatory variables.

Student Effort Hours:
Student Effort Type Hours
Lectures

24

Tutorial

10

Computer Aided Lab

10

Autonomous Student Learning

70

Total

114

Approaches to Teaching and Learning:
Weekly video lectures.
Weekly labs covering materials implementation in R.
Weekly problem sets.

Two Assignments
One Exam
Requirements, Exclusions and Recommendations
Learning Requirements:

Students are expected to have taken two previous statistics/data analytics modules, such as STAT40720 and STAT30280

Module Requisites and Incompatibles
Pre-requisite:
STAT30280 - Inference for Data Analyti(OL), STAT40720 - Intro Data Analytics (Online)

Incompatibles:
FIN30520 - Machine Learning Finance, STAT30240 - Predictive Analytics I

Assessment Strategy
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Exploratory Data Analysis
Regression Modelling
Varies over the Trimester n/a Standard conversion grade scale 40% No

30

Examination: 2 hour end of trimester online written exam 2 hour End of Trimester Exam No Standard conversion grade scale 40% No

70

Carry forward of passed components
No

Resit In Terminal Exam
Spring Yes - 2 Hour
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Feedback on assignments provided to students post-assessment

1. Linear Models with R, Second Edition by Julian J. Faraway
2. Applied Regression Analysis and Generalized Linear Models, Third Edition by John Fox
3. An R Companion to Applied Regression, Third Edition by John Fox
4. An R Companion to Linear Statistical Models by Christopher Hay-Jahans
Name Role
Ms Prabhleen Kaur Lecturer / Co-Lecturer
Ms Courtney Clarke 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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