Predictive Analytics I (STAT30240)

Credits 
5 
Subject 
Statistics & Actuarial Science 
Level 
3 
School 
Mathematics and Statistics 
Semester 
Autumn 
Module Coordinator 
Dr Michelle Carey 
Topics covered:
1. Matrix revision and Exploratory data analysis;
2. Simple linear regression (SLR): properties of least squares estimates; ttests; Ftests; Confidence intervals; Prediction intervals; Complete SLR analysis in the R statistical software;
3. Multiple linear regression (MLR): properties of least squares estimates; ttests; Ftests; Confidence intervals; Prediction intervals; Complete MLR analysis in the R statistical software;
4. Weighted Least Squares Estimation.
5. Regression Diagnostics and Leverage, Influence and Outliers
6. Model Building; Transformations and Interactions
All the material is supplemented with its implementation in the R programming language which is rated 7th in IEEE list of top programming languages.
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.
 Hrs/Semester 
Lectures  24 
Tutorial  12 
Laboratories  12 
Autonomous Student Learning  72 
Total Workload  120 
 % of Final Grade  Timing 
Continuous Assessment: Lab Quizzes  5  Varies over the Trimester 
Continuous Assessment: Project  20  Varies over the Trimester 
Examination: Written examination  75  2 hour End of Trimester Exam 
Compensation 
This module is not passable by compensation 
Resit Opportunities 
End of Semester Exam 
Remediation 
If you fail this module you may repeat, resit or substitute where permissible 
Module Requisites and Incompatibles 
PreRequisite : 
Required : 
CoRequisite : 
Incompatibles : 
Additional Information : 
Equivalent Modules 
Predictive Analytics I (STAT40670) 
Prior Learning 
Requirements: 
A good understanding of statistics at an introductory level, including ttests, correlation and covariance, and properties of the expectation and variance operators 
Excluded: 

Recommended: 

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