ST2LM-Linear Models

Module Provider: Mathematics and Statistics
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Autumn term module
Pre-requisites: ST1PS Probability and Statistics
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Module version for: 2017/8

Module Convenor: Dr Karen Ayres

Email: k.l.ayres@reading.ac.uk

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.

Aims:
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.

Assessable learning outcomes:
On completion of this module students will have acquired:

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

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, transformations.
Further topics: variable selection, models with continuous and categorical variables
Use of SAS to fit linear models.

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

Contact hours:
  Autumn Spring Summer
Lectures 20
Tutorials 6
Practicals classes and workshops 4
Guided independent study 70
       
Total hours by term 100.00
       
Total hours for module 100.00

Summative Assessment Methods:
Method Percentage
Set exercise 20
Class test administered by School 80

Other information on summative assessment:
Two assignments and one class test.

Formative assessment methods:
Problem sheets.

Penalties for late submission:
The Module Convenor will apply the following penalties for work submitted late, in accordance with the University policy.

  • where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the 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: http://www.reading.ac.uk/web/FILES/qualitysupport/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.

    Length of examination:
    N/A

    Requirements for a pass:
    A mark of 40% overall

    Reassessment arrangements:
    One examination paper of 2 hours duration in August/September - the resit module mark will be the higher of the exam mark (100% exam) and the exam mark plus previous coursework marks (80% exam, 20% coursework).

    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: 31 March 2017

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