CE3STS-Statistical analysis 2

Module Provider: School of Construction Management and Engineering, School of Built Environment
Number of credits: 10 [5 ECTS credits]
Level:6
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Dr Christos Halios
Email: c.halios@reading.ac.uk

Type of module:

Summary module description:

This module introduces principal methods to assess statistical data applicable to engineering subjects including Architectural Engineering. Using a number of datasets from a range of science and engineering applications, students will learn practical statistical techniques, as well as using software to analyse data. This module builds upon the previous basic statistical knowledge gained in the module of Statistical Analysis 1 (CE2STS) and provides more advanced statistical techniques for testing hypotheses, conducting principal component and clustering analyses. In addition, student will learn the main differences between parametric and nonparametric analysis. Ìý

Aims:

The aim of this module is to provide students with a number of key statistical data analysis techniques require for the evaluation of a dataset and drawing an informed and unbiased conclusion.

Assessable learning outcomes:

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

• Select and apply appropriate statistical techniques for exploring a set of data,


• Explain the difference between the normal and non-normal distribution of data,


• Apply and test the hypothesis for a dataset,Ìý


• Select appropriate techniques to find any associations between variables in a dataset,


• Categorise variables in a dataset and conduct a clustering analysis,


• Apply statistical methods to engineering applications using statistical software.

Additional outcomes:

• To explain assumptions underlying statistical techniques,


• To present and interpret statistical results scientifically,


• To learn how to use statistical software to apply statistical techniques.Ìý

Outline content:

• parametric vsÌýnonparametricÌýstatistics


• Normality testing


• Hypothesis testing


• Association between continuous variables


• Association between discrete variables


• Factorial experiments


• Clustering analysis


• Principal component analysis


• Bayesian statistics

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	100	0	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 autumn term. In addition, the outcomes of project work should be prepared as a report (2000-2500 words) and should be submitted online by the end of week 11 of the autumn 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 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: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/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.

Assessment requirements for a pass:

A mark 0f 40%

Reassessment arrangements:

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

Additional Costs (specified where applicable):

Last updated: 23 January 2023