ST4MSD-Modelling Structured Data

Module Provider: Mathematics and Statistics
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites: ST2LM Linear Models or ST2LMD Linear Models and Data Analysis
Non-modular pre-requisites:
Co-requisites:
Modules excluded: ST3MSD Modelling Structured Data
Current from: 2019/0

Module Convenor: Dr Fazil Baksh

Email: m.f.baksh@reading.ac.uk

Type of module:

Summary module description:

Statistical modelling techniques for datasets with more than one response measure or more than one level of variation are covered. Examples include data from nested designs, split-plot and repeated measures experiments. Methods such as the summary statistics approach, split-plot and multivariate analysis of variance will be described. Mixed models have become a popular method for analysing structured data. Such models will be considered in detail.

Aims:

• to provide students an appreciation of the issues surrounding the analysis of structured data;


• to describe a range of statistical methods and models for the analysis of structured data;


• to provide students the ability to identify and apply appropriate techniques for such data;


• to provide students with a theoretical understanding of statistical methods and models for structured data.

Assessable learning outcomes:

By the end of the module it is expected that the student will have:

• an awareness of structured data, in particular repeated measurements, and methods for analysing data of this type;


• the ability to compare and contrast different approaches for analysing structured data and be able to perform common types of analysis and interpret results.


• an understanding and appreciation of the theory underlying methodology for structured data.

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This module will be assessed to a greater depth than the excluded module ST3MSD.

Additional outcomes:

Outline content:

Summary statistics for repeated measures data; Split-plot analysis of variance; Multivariate analysis of variance; Mixed models - marginal and random coefficient models; Maximum likelihood and REML fitting methodologies; Use of SAS PROC GLM and PROC MIXED.

Brief description of teaching and learning methods:

Lectures supported by problem sheets and practicals.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures		16
Practicals classes and workshops		4
Guided independent study:		80
Ìý	Ìý	Ìý	Ìý
Total hours by term		100
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Written exam	100

Summative assessment- Examinations:

One examination of two hours duration

Summative assessment- Coursework and in-class tests:

Formative assessment methods:

Self-study exercise sheets are provided

Penalties for late submission:
The Module Convener 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[1] (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:

50%

Reassessment arrangements:

Written exam of 2 hours duration

Additional Costs (specified where applicable):

Cost	Amount
Required text books	Ìý
Specialist equipment or materials	Ìý
Specialist clothing, footwear or headgear	Ìý
Printing and binding	Ìý
Computers and devices with a particular specification	Ìý
Travel, accommodation and subsistence	Ìý

Last updated: 8 April 2019