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MA2251 Linear Regression Models

MA2251 Linear Regression Models

Credits: 10 Convenor: Dr. M.J. Phillips Semester: 2 (weeks 1 to 6)
Prerequisites: essential: MA1061, MA2201
Assessment: Examination and coursework: 80:20% Examination: 0%
Lectures: 18 Problem Classes: 3
Tutorials: none Private Study: 51
Labs: 3 Seminars: none
Project: none Other: none
Surgeries: none Total: 75

Subject Knowledge

Aims

This module covers the simple linear regression model as an introduction to the general linear model. An introduction to the least squares estimation of regression lines aims to show how linear relationships can be established between two variables. This module aims to present the method of least squares estimation of the linear model parameters and the methods of making inferences about these parameters through the calculation of confidence intervals and the use of hypothesis tests. In particular it is an aim of this course to impart understanding of how statistical models explain variation. The aim of covering the theory for the general linear regression model is to demonstrate the extension of the theory to cover multiple linear regression and polynomial regression.

Learning Outcomes

Students should know the distributions of the least squares estimators for parameters of the simple linear regression model, be able to calculate confidence intervals and prediction intervals and use hypothesis tests for model parameters. Students should be able to perform hypothesis tests using an analysis of variance (ANOVA) table and to understand how it is used to explain variation, and be able to assess the lack of fit of a linear model using repeated observations.

Methods

Class sessions with some handouts.

Assessment

Marked problem sheets and examination.

Subject Skills

Aims

To provide students with the ability to calculate estimates and make inferences about the parameters of the simple linear regression model, and the knowledge of using an analysis of variance (ANOVA) table to explain variation, as well as the knowledge of the theory of the general linear model.

Learning Outcomes

Students will have worked on a number of statistical problems, drawn conclusions and have written up their results with the aid of computer resources. Students will be able to use the techniques taught within the module to answer statistical questions, and be able to present arguments and solutions in a coherent form.

Methods

Computer classes.

Assessment

Marked problem sheets and examination.

Explanation of Pre-requisites

The module MA1061 provides the basic ideas of probability which are required to understand statistical methods. The module MA2201 presents the basic concept of a statistic and its use in estimation and hypothesis testing. There is a brief introduction to the basic methods of data analysis based on the classical normal theory results of mathematical statistics.

Course Description

The single most important method in statistical analysis is the use of the simple linear regression model which is the simplest example of the general linear model. The prime objective of an analysis using this model is the determination of how the explanatory variable is related to the response variable. It is possible to associate confidence intervals to estimates or predictions obtained from the model and assign p-values to hypotheses to be tested. The analysis of the model is based on the method of least squares estimation, where the `residual sum of squares' not only provides an estimate of the error variance but, perhaps more importantly, also offers a method for assessing the acceptability of any proposed model.

Note: This module cannot be taken in conjunction with MA2261.

Syllabus

Relationships between variables are considered. Inferences about the linear association of two variables are studied using the coefficient of correlation. The simple linear regression model is briefly explained. Least squares estimators of slope and intercept parameters are obtained. Sampling distributions are derived for these estimators as well as for `error' variance parameter. Inference for the model parameters is covered using confidence intervals, prediction intervals and hypothesis tests. Calibration. Least squares estimators of the parameters of some other simple models are obtained. Use of residuals for checking model assumptions. Analysis of variance. Distributions of sums of squares of standard normal variables. Lack of fit using repeated observations. The theory of the general linear model.

Reading list

Recommended:

W. Mendenhall, R. L. Scheaffer and D. D. Wackerly, Mathematical Statistics with Applications, 6th edition, Duxbury Press, 2002.


Resources

Problem sheets, computer laboratory, lecture rooms.

Module Evaluation

Module questionnaires, module review, year review.

Next: MA2261 Linear Statistical Models Up: ModuleGuide03-04 Previous: MA2201 Introductory Statistics

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Last updated: 2004-02-21
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