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MMath Mathematics / Course details

Year of entry: 2021

Course unit details:
Regression Analysis

Unit code MATH38141
Credit rating 10
Unit level Level 3
Teaching period(s) Semester 1
Offered by Department of Mathematics
Available as a free choice unit? No

Overview

In many areas of science, technology and medicine one often wishes to explore the relationship between one observable random response and a number of explanatory variables, which may influence simultaneously the response. The required statistical principles and techniques are established and used to select a suitable model for a given dataset.

Pre/co-requisites

Unit title Unit code Requirement type Description
Probability 2 MATH20701 Pre-Requisite Compulsory
Statistical Methods MATH20802 Pre-Requisite Compulsory

None.

Aims

  • To familiarise students with the methodology and applications of standard techniques of regression analysis and analysis of variance.
  • To explore some of the wide range of real-life situations occurring in different fields that can be investigated using regression statistical models.

Learning outcomes

On successful completion of this course unit students will be able to: 

  • formulate, estimate, use and test for lack of fit 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;
  • state the implications of orthogonality and collinearity between regressors;
  • obtain a best-fitting model in a systematic and pragmatic way;
  • use R to implement methods covered in the course.

Syllabus

  • Regression models. Assumptions. Matrix representation. Least squares estimators and their properties. Fitted values. Residuals. Estimating 2. [4]
  • Vector random variables. Gauss-Markov theorem. Multivariate normal distribution. [3]
  • Distribution of estimators and residuals. [3]
  • Orthogonality. Multicolinearity. Indicator variables. Overparameterisation. [2]
  • Estimating variance from replication. Weighted least squares. Testing model fit with and without replication. Checking model assumptions. Plots of residuals.[3]
  • Model building and model selection. Deleting predictor variables. The general linear hypothesis. Stepwise regression. Penalised likelihood. AIC, AICc, BIC. Comparison of nested and not nested models. [3]
  • One and two way analysis of variance. [4]

Assessment methods

Method Weight
Other 30%
Written exam 70%
  • Coursework: weighting 30%
  • End of semester examination: weighting 70%

Feedback methods

Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding.  Coursework or in-class tests (where applicable) also provide an opportunity for students to receive feedback.  Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.
 

Recommended reading

  • Draper, D. N. R. and Smith, H., Applied Regression, (third edition).  Wiley.
    Faraway, J. J. (2015). Linear Models with R., (second edition). Chapman and Hall/CRC.Montgomery,
    D. C. and Peck, E. A., (2011). Introduction to Linear Regression Analysis, Wiley.
    Weisberg, S., (2013). Applied Linear Regression (fourth edition). Wiley.
    All are both recommended and further reading.

Study hours

Scheduled activity hours
Lectures 22
Practical classes & workshops 11
Independent study hours
Independent study 67

Teaching staff

Staff member Role
Alexander Donev Unit coordinator

Additional notes

This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact.

Please see Blackboard / course unit related emails for any further updates.

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