ST2LMD: Linear Models and Data Analysis

Module code: ST2LMD

Module provider: Mathematics and Statistics; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: Level 2 (Intermediate)

When you'll be taught: Semester 1

Module convenor: Dr Karen Poulter, email: k.l.poulter@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s): BEFORE OR WHILE TAKING THIS MODULE YOU MUST TAKE ST1PS OR TAKE ST1PSNU OR TAKE EC120 OR TAKE EC204 (Compulsory)

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 21 May 2024

Overview

Module aims and purpose

Linear models are used widely in statistics and supervised machine learning. The aim of this module is to describe the most common models, including multiple linear regression for observational studies and completely randomised designs for planned studies, and to explore their relationship to the general linear model. The module will introduce students to key principles in the design of planned experiments, where the aim is to assess the comparative effects of treatments and factors influencing a response. Alongside design principles, different experimental designs and the practicalities associated with them will be discussed, and methods for analysing data from different types of design will be described. The module aims to provide the skills to develop and test linear models appropriate for a range of practical problems, as well as provide further experience of real-life data analysis, including communicating the results of a data analysis exercise. Students will benefit from learning how to analyse data using software packages, and will act as statistical consultants in a group setting. 

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Recognise which models are appropriate for different types of data
2. Fit and interpret appropriate models, and check their adequacy
3. Describe and apply the statistical principles of good experimental design
4. Analyse data using statistical software, interpret the results and communicate them clearly

Module content

• Simple linear regression.
• The completely randomised design.
• Experimental design: principles, randomised block designs, factorial designs.
• The General Linear Model for multiple regression: definition and matrix notation.
• Model checking: residual analysis, influential observations.
• Further topics: ANCOVA, variable selection, polynomial regression, multicollinearity.
• Use of SAS and R for data analysis.
• Data analysis fundamentals: cleaning data, writing reports and giving presentations.
• Statistical consultancy skills.

Structure

Teaching and learning methods

The material is delivered via lectures and screencasts supported by tutorials which tackle non-assessed exercises, together with computer based practical work. Online drop-in help and early feedback sessions will be provided to support the group data analysis exercise. 

Study hours

At least 51 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

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

Assessments with numerical marks

• 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 three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

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.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Non-assessed problem sheets with solutions and generic feedback. There will also be a formative presentation for the group data analysis exercise, before the summative report is due for submission. 

Reassessment

Additional costs