ST2LMD-Linear Models and Data Analysis

Module Provider: Mathematics and Statistics
Number of credits: 20 [10 ECTS credits]
Terms in which taught: Autumn / Spring term module
Pre-requisites: ST1PS Probability and Statistics
Non-modular pre-requisites:
Modules excluded: ST2LM Linear Models
Current from: 2020/1

Module Convenor: Miss Hannah Fairbanks


Type of module:

Summary module description:

This module covers the most common models used in statistics: multiple linear regression for observational studies and completely randomised designs for planned studies. It  also provides students with experience of real-life data analysis based on topics in applied statistics (such as medical statistics, sampling, demography, statistical genetics, forensic statistics and non-parametric statistics) by considering different datasets, how to analyse them, and how to present results from their analysis.


Linear models are used widely in statistics. The most common models will be reviewed and their relationship to the general linear model explored. The main aim of the module is to present a standard approach for fitting linear models to data and for comparing alternative linear models with one another. The module also aims to provide the skills to develop and test linear models appropriate for a range of practical problems. Alongside this the module aims to broaden students' experience of the use of the statistics which they learn in this and other modules; to give students experience of real-life data analysis by considering datasets and how to analyse them; to further develop skills in teamwork; to give students practice in preparing short reports and presentations, and organising statistical ideas.

Assessable learning outcomes:

On completion of this module students will have acquired:

  • the ability to use R to perform appropriate statistical analysis; ;

  • knowledge of the theory associated with the general linear model and the principles of analysis of variance;

  • the ability to fit regression models to data, interpret them and check their adequacy; an awareness of the role of regression modelling in the analysis of data from desi gned experiments;

  • the ability to use SAS to fit linear models and check their adequacy;

  • the ability to identify which statistical techniques are appropriate for certain types of data, in an open-ended setting;

  • the ability to identify the important questions of interest as part of a general description of a problem;

  • the ability to elicit information from others and to translate vaguely expressed concepts into formal statistical think ing;

  • the ability to clearly communicate results (in a report and in a presentation); the skills needed for working in a statistics project team.

Additional outcomes:

Outline content:

Simple linear regression and the completely randomised design. The General Linear Model for multiple regression: definition and matrix notation. Model checking: residual analysis, influential observations. Further topics: polynomial regression, ANCOVA, variable selection, multicollinearity. Use of SAS and R for data analysis. Data analysis fundamentals based on applied statistics topics: cleaning data, formulating statistical questions. Statistical consultancy skills .

Brief description of teaching and learning methods:
Lectures, supported by problem sheets, PC practicals and tutorials.

Contact hours:
  Autumn Spring Summer
Lectures 20 11
Seminars 2
Tutorials 5 1
Practicals classes and workshops 5 6
Guided independent study: 70 80
Total hours by term 0
Total hours for module 200

Summative Assessment Methods:
Method Percentage
Written exam 35
Report 20
Oral assessment and presentation 10
Set exercise 35

Summative assessment- Examinations:

One 2 hour written exam.

Summative assessment- Coursework and in-class tests:

One group presentation, one group data analysis report and one group managers summary report, plus a number of individual assignments, and assignments for which it is your choice to work individually or in a small group.

Formative assessment methods:

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

  • where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
  • where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:
A mark of 40% overall

Reassessment arrangements:
One examination of 2 hours duration in August/September (50%) and one data analysis report (50%).

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 11 February 2021


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