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BSc Computer Science and Mathematics with Industrial Experience

Year of entry: 2024

Course unit details:
Linear Regression Models

Course unit fact file
Unit code MATH27711
Credit rating 10
Unit level Level 2
Teaching period(s) Semester 1
Available as a free choice unit? No

Overview

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. 

Pre/co-requisites

Unit title Unit code Requirement type Description
Linear Algebra MATH11022 Pre-Requisite Compulsory
Probability I MATH11711 Pre-Requisite Compulsory
Statistics I MATH11712 Pre-Requisite Compulsory

Aims

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.

Learning outcomes

  • 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.

Assessment methods

Method Weight
Other 20%
Written exam 80%

Written Exam - 80%


One mid-term online timed Blackboard test - 20%
 

Feedback methods

Generic feedback will be provided after marks are released

Recommended reading

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
 

Study hours

Scheduled activity hours
Lectures 22
Practical classes & workshops 6
Tutorials 6
Independent study hours
Independent study 66

Teaching staff

Staff member Role
Wentao Li Unit coordinator

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