BSc Actuarial Science and Mathematics / Course details
Year of entry: 2024
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Course unit details:
Linear Regression Models
|Unit level||Level 2|
|Teaching period(s)||Semester 1|
|Available as a free choice unit?||No|
In many areas of science, technology and medicine, researchers are often interested in two objectives: one is to explore the relationship between one observable random response and a number of explanatory variables; the other is to analyze the variability of the responses. Many statistical techniques investigate these objectives through the use of linear regression models. This course presents the theory and practice of these models.
|Unit title||Unit code||Requirement type||Description|
The particular aims are to enable the students to:
1. Understand linear regression model with one or multiple independent variables.
2. Understand general linear model with continuous independent variables.
3. Understand classification models for one and two factors.
4. Understand ANCOVA models for one factor and multiple continuous independent variables.
- formulate, estimate and use regression linear models that are suitable for relevant statistical studies
- formulate statistical hypotheses in terms of the model parameters and test such hypotheses
- obtain confidence intervals for linear combinations of the model parameters
- obtain prediction intervals for linear combinations of future responses
- identify the impact of outliers on regression line
- use R to implement methods covered in the course
Teaching and learning methods
Teaching is composed of two hours of lectures per week and one tutorial class per fortnight. And one Examples class in the week there is no tutorial. Some lecture time will be delivered through pre-recorded videos posted online. Teaching materials will be uploaded to Blackboard for reference and review.
Written Exam - 80%
One mid-term online timed Blackboard test - 20%
Generic feedback will be provided after marks are released
1. Kutner, M. H., Nachtsheim, C. J., Neter, J. & Li, W. (2005). Applied Linear
Statistical Models. (5th edition). McGraw-Hill/Irwin: Boston.
2. Montgomery, D. C. & Peck, E. A. (1992). Introduction to Linear Regression
Analysis (5th edition). Wiley: New York.
3. Weisberg, S., (2013). Applied Linear Regression (4th edition). Wiley.
4. James H. Stapleton, (2009). Linear Statistical Models. (2nd edition) John Wiley & Sons
|Scheduled activity hours|
|Practical classes & workshops||6|
|Independent study hours|
|Wentao Li||Unit coordinator|