CE2STS-Statistical analysis 1

Module Provider: School of Construction Management and Engineering, School of Built Environment
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2020/1

Module Convenor: Dr Eugene Mohareb

Email: e.mohareb@reading.ac.uk

Type of module:

Summary module description:

This module introduces key statistical methods to assess real-world engineering problems. It explains how to use basic statistical tools and introduces quantitative data analysis methods that are useful in engineering subjects including Architectural Engineering. Using a number of datasets from a range of science and engineering applications, students will learn practical statistical techniques and fundamental principles, as well as usingÌýa software to analyse data. This module leads on to the study of more advanced statistical techniques including probability analysis in the module of Statistical Analysis 2 (CE3STS).

Aims:

The aim of this module is to provide students with principles of statistical data analysis requiredÌýfor the evaluation of a dataset and drawing an informed and unbiased statistical conclusion.

Assessable learning outcomes:

On successful completion of this module the student should be able to:

• Explain assumptions underlying statistical techniques,


• Present and interpret statistical results scientifically,


• Select and apply statistical techniques for exploring data,


• Apply statistical methods using statistical software,


• Determine the effect of samples and populations on the results of statistical analysis,

Perform data fitting and to evaluate the fit results, including errors and goodness-of-fit.

Additional outcomes:

• To explain how errors may be propagated in the process of statistical analysis,


• To apply linear and nonlinear regression on a set of data,


• To explain the limitations of regression analysis.

Outline content:

• The basic concept of measurement


• Samples and populations


• Inferential vs descriptive statistics


• Analysis of variance and covariance


• Linear regression analysis


• Nonlinear regression analysis


• Confidence intervals


• Multivariable regression analysis


• Propagation of error


• Presenting and summarising data

Global context:

The skills and knowledge that students will acquire from this module have global applications.

Brief description of teaching and learning methods:

Teaching in this module will be by means of lectures, tutorials and practical classes using facilities available in the computer laboratory. These sessions will be complemented by project activities and guided independent study.

Independent study hours needed depend on the learning style of each individual. The following guide for independent study hours is just an example.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures		20
Tutorials		5
Practicals classes and workshops		5
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (independent)		10
Ìý Ìý Wider reading (directed)		5
Ìý Ìý Peer assisted learning		5
Ìý Ìý Advance preparation for classes		10
Ìý Ìý Preparation for tutorials		5
Ìý Ìý Preparation of practical report		25
Ìý Ìý Revision and preparation		8
Ìý Ìý Reflection		2
Ìý	Ìý	Ìý	Ìý
Total hours by term	0	100	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Project output other than dissertation	60
Set exercise	40

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

There will be a set exercise test that will be assessed summatively and should be submitted online by the end of week 9 of the spring term. In addition, the outcomes of project work should be prepared as a report (1500-2000 words) and should be submitted online by the end of week 11 of the spring term.

Formative assessment methods:

This module includes formative assessment of exercises and problem-solving practices about statistical techniques and their applications that will be discussed in tutorials, practical classes and workshops.

Penalties for late submission:

The Module Convenor 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:

A mark 0f 40%

Reassessment arrangements:

Students who have failed in their first attempt will be providedÌýwith a resit project brief and they should submit a project report (2500-3000) online.

Additional Costs (specified where applicable):

Last updated: 29 May 2020