# 2024/25 Taught Postgraduate Module Catalogue

## OMAT5201M Linear Modelling

### 15 creditsClass Size: 100

Module manager: TBC
Email: .

Taught: 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr) View Timetable

Year running 2024/25

None

### Pre-requisites

 OMAT5101M Statistical Methods OMAT5102M Exploratory Data Analysis

Module replaces

N/A

This module is not approved as an Elective

### Module summary

In many areas of science and social study, several variables or measurements are taken from each member of a sample, with one variable regarded as an ‘outcome’ and the others regarded as ‘predictors’ of the outcome. This module will examine ways of predicting one particular variable from the remaining measurements using the linear regression model. The general theory of linear regression models will be covered, including variable selection, tests and diagnostics and methods to deal with outliers. While linear regression is a tremendously useful statistical method, it has limitations. Generalised linear models extend linear regression in many ways - allowing us to analyse more complex data sets. In this module we will see how to combine continuous and categorical predictors, analyse binomial response data and model count data.

### Objectives

The module will equip students with understanding of the theory of linear models and be able to fit multiple linear regression models to data and interpret the results.  The content will develop an appreciation of the limitations of linear models and the use of link functions to generalise the linear regression model.  In particular, the module will explore logistic regression and log linear models.

Learning outcomes
On completion of this module students should be able to:

1. Fit multiple linear regression models to data, and interpret the models;
2. Apply methods of robust regression;
3. Carry out regression analysis with generalised linear models including the use of link functions;
4. Understand and employ methods for model selection.

Skills outcomes
Skills developed in this module include:

- evaluating the quality of data and selecting suitable methods of analysis;
- fitting models to data and interpreting the results;
- communicating the outcome of data analysis.

### Syllabus

Indicative content for this module includes:

1. Linear regression.
2. Robustness.
3. Generalised Linear Models.

### Teaching methods

 Delivery type Number Length hours Student hours On-line Learning 1 1.50 1.50 On-line Learning 5 1.00 5.00 Discussion forum 6 2.00 12.00 Independent online learning hours 42.00 Private study hours 89.50 Total Contact hours 18.50 Total hours (100hr per 10 credits) 150.00

### Opportunities for Formative Feedback

Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow opportunities for formative feedback from peers and tutors.

### Methods of assessment

Coursework
 Assessment type Notes % of formal assessment Online Assessment MCQ test and short answer questions 20.00 Assignment Project Report 80.00 Total percentage (Assessment Coursework) 100.00

Students will resit by completing the Assignment (which covers all learning outcomes) at the next running of the module.

### Reading list

There is no reading list for this module

Last updated: 29/04/2024 16:18:46

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