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Course Module Information
Course Modules
ST313: Applied Regression Models
Semester 1 | Credits: 5
An introduction to the theory and application of regression models. Topics covered include the simple linear model, least-squares estimation, multiple linear regression, inference, model checking, model choice and variable selection, and the use of Minitab for practical applications.
(Language of instruction: English)
Learning Outcomes
- calculate and interpret correlations between variables and make inferences about relationships
- formulate a linear regression model, calculate estimated coefficients and make statistical inferences on the fitted model using both parameter estimates and the ANOVA table
- obtain fitted values and predictions at new data points, together with associated confidence intervals
- calculate regression diagnostics and use these to check model assumptions, including linearity, normality, constant variance, independence and the presence of outliers and influential points
- formulate a multiple regression model and specify this in matrix form
- derive least-squares estimates for
- Use Principal Components Analysis to reduce the dimensionality of a complex data set
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers
- COLLETTE MCLOUGHLIN:
Research Profile |
Email
- HAIXUAN YANG:
Research Profile |
Email
Reading List
- "Applied linear statistical models" by Michael H Kutner
ISBN: ISBN007238688.
Note: Module offerings and details may be subject to change.