Module manager: Dr Stuart Barber
Email: s.barber@leeds.ac.uk
Taught: Semester 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr) View Timetable
Year running 2024/25
None
| OMAT5101M | Statistical Methods |
| OMAT5102M | Exploratory Data Analysis |
N/A
This module is not approved as an Elective
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.
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.
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 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.
Indicative content for this module includes:
1. Linear regression.
2. Robustness.
3. Generalised Linear Models.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| On-line Learning | 1 | 1.5 | 1.5 |
| On-line Learning | 5 | 1 | 5 |
| Discussion forum | 6 | 2 | 12 |
| Independent online learning hours | 42 | ||
| Private study hours | 89.5 | ||
| Total Contact hours | 18.5 | ||
| Total hours (100hr per 10 credits) | 150 | ||
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.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Online Assessment | MCQ test and short answer questions | 20 |
| Assignment | Project Report | 80 |
| Total percentage (Assessment Coursework) | 100 | |
Students will resit by completing the Assignment (which covers all learning outcomes) at the next running of the module.
There is no reading list for this module
Last updated: 9/5/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team