QLTY08016 2019 Experimental Design

General Details

Full Title
Experimental Design
Transcript Title
Experimental Design
N/A %
Subject Area
QLTY - Quality
MEMA - Mech and Manufact Eng
08 - NFQ Level 8
05 - 05 Credits
Start Term
2019 - Full Academic Year 2019-20
End Term
9999 - The End of Time
Programme Membership
SG_EADVA_E08 201900 Level 8 Certificate in Engineering in Advanced Lean Sigma Quality SG_EPOLY_K08 201900 Bachelor of Engineering (Honours) in Polymer Processing SG_EQLTY_K08 201900 Bachelor of Science (Honours) in Engineering in Quality Management and Tech SG_EPOLP_K08 202200 Bachelor of Engineering (Honours) in Polymer Process Engineering

This module will provide the student with the tools necessary to plan, conduct and analyse experiments. The analytical interpretation of these results will allow the student to optimise products and processes.

Learning Outcomes

On completion of this module the learner will/should be able to;


Calculate correlation coefficient and conduct a test of significance.


Solve simple linear regression and curvilinear regression problems and make predictions.


Conduct one way and two-way ANOVA including the analysis of residuals.


Conduct  and  factorial experiments and analyse the resulting data.


Apply Taguchi methods involving calculation of loss function and signal-to-noise ratios.


Describe when and how to apply the appropriate experimental techniques and models


Use a statistical package to analyse and interpret experimental data.

Teaching and Learning Strategies

Real world examples, case studies and published peer reviewed papers will be utilised, where possible.

Module Assessment Strategies

Students will plan, conduct, analyse and interpret their own non-industrial (non-work related) experiment using Minitab software.

Final 2.5 hour written exam.

Repeat Assessments


Module Dependencies

MATH08005 201300 Statistics

Indicative Syllabus

  1. Correlation : Meaning of correlation coefficient (r).Hypothesis test on correlation coefficient. Spearman's Rank Order correlation. Statistical significance of r
  2. Regression : Simple linear regression. Making predictions. Confidence intervals and hypothesis testing. The coefficient of Determination. Curvilinear regression and use of transformations. Computation of Multiple Regression via computer.
  3. Analysis of Variance : One and two way ANOVA. Comparison of treatment means - Least Significant difference. Analysis of Residuals. Randomised block designs.
  4.  Two level Factorial designs : Planning and conducting industrial experiments, blocking, replication, randomisation. Analysis of variance. Calculation of main and interaction effects. Development of effects/response graphs. Development of empirical model. Model adequacy checking. Dealing with single replicates of a  design. Construction of blocks in a  design.
  5. Two level Fractional factorial designs : Their construction and analysis. Design resolution, confounding patterns and generating relations. Fold-over designs.
  6. Introduction to Response Surface methods. Path of steepest ascent. Central Composite designs. Use of response optimiser in Minitab.
  7. Taguchi methods: The philosophy. Loss function. Approach to parameter and tolerance design. Inner and outer Arrays. Linear graphs, Data analysis using Taguchi methods. Comparison of Taguchi experimental designs and data analysis methods with western methods.
  8. Brief introduction to Random vs. Fixed effects models and Crossed vs. Nested designs.  

Indicative Projects  

  1. Optimisation of a catapult
  2. Optimise the cooking of Rice
  3. Optimisation of a paper airplane

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
20 %
End of Semester / Year Formal Exam
80 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Group Project Students will plan, design, conduct, analyse and interpret their own non-industrial experiment Continuous Assessment UNKNOWN 20 % OnGoing 1,2,3,4,5,6,7

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Final Exam - one 2.5 Hour written paper Final Exam Closed Book Exam 80 % End of Term 1,2,3,4,5,6,7

Full Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Not Specified Lecture 2 Weekly 2.00
Tutorial Not Specified Tutorial 2 Weekly 2.00
Independent Learning UNKNOWN Independent Learning 4 Weekly 4.00
Total Full Time Average Weekly Learner Contact Time 4.00 Hours

Part Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Distance Learning Suite Theory 2.5 Weekly 2.50
Tutorial Not Specified Tutorial 0 Weekly 0.00
Independent Learning UNKNOWN Independent Learning 4 Weekly 4.00
Total Part Time Average Weekly Learner Contact Time 2.50 Hours

Required & Recommended Book List

Required Reading
2017 Design and Analysis of Experiments John Wiley & Sons
ISBN 9781119113478 ISBN-13 1119113474

TRY (FREE for 14 days), OR RENT this title: www.wileystudentchoice.com Design and Analysis of Experiments, 9th Edition continues to help senior and graduate students in engineering, business, and statistics-as well as working practitioners-to design and analyze experiments for improving the quality, efficiency and performance of working systems. This bestselling text maintains its comprehensive coverage by including: new examples, exercises, and problems (including in the areas of biochemistry and biotechnology); new topics and problems in the area of response surface; new topics in nested and split-plot design; and the residual maximum likelihood method is now emphasized throughout the book.

Required Reading
2005-05-31 Statistics for experimenters Wiley-Blackwell
ISBN 0471718130 ISBN-13 9780471718130

A Classic adapted to modern times Rewritten and updated, this new edition of Statistics for Experimenters adopts the same approaches as the landmark First Edition by teaching with examples, readily understood graphics, and the appropriate use of computers. Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis. Providing even greater accessibility for its users, the Second Edition is thoroughly revised and updated to reflect the changes in techniques and technologies since the publication of the classic First Edition. Among the new topics included are: Graphical Analysis of Variance Computer Analysis of Complex Designs Simplification by transformation Hands-on experimentation using Response Service Methods Further development of robust product and process design using split plot arrangements and minimization of error transmission Introduction to Process Control, Forecasting and Time Series Illustrations demonstrating how multi-response problems can be solved using the concepts of active and inert factor spaces and canonical spaces Bayesian approaches to model selection and sequential experimentation An appendix featuring Quaquaversal quotes from a variety of sources including noted statisticians and scientists to famous philosophers is provided to illustrate key concepts and enliven the learning process. All the computations in the Second Edition can be done utilizing the statistical language R. Functions for displaying ANOVA and lamba plots, Bayesian screening, and model building are all included and R packages are available online. All theses topics can also be applied utilizing easy-to-use commercial software packages. Complete with applications covering the physical, engineering, biological, and social sciences, Statistics for Experimenters is designed for individuals who must use statistical approaches to conduct an experiment, but do not necessarily have formal training in statistics. Experimenters need only a basic understanding of mathematics to master all the statistical methods presented. This text is an essential reference for all researchers and is a highly recommended course book for undergraduate and graduate students.

Required Reading
2017-03-07 Design and Analysis of Experiments Springer
ISBN 3319522485 ISBN-13 9783319522487

This book offers a step-by-step guide to the experimental planning process and the ensuing analysis of normally distributed data, emphasizing the practical considerations governing the design of an experiment. Data sets are taken from real experiments and sample SAS programs are included with each chapter. Experimental design is an essential part of investigation and discovery in science; this book will serve as a modern and comprehensive reference to the subject.

Required Reading
2014-05-15 A Doe Handbook Createspace Independent Publishing Platform
ISBN 1497511909 ISBN-13 9781497511903

This short handbook is a practical and accessible guide to the statistical design and analysis of 2-level, multi-factor experiments of the kind widely used in industry and business. Written for technologists and researchers, it forgoes the usual heavy statistical overlay of typical texts on this subject by focusing on a limited catalog of standard designs that are useful for commonly encountered problems. These design choices are based on relatively recent developments in design projectivity, and their analysis requires nothing more than simple plots of the data: neither special expertise nor complex software is needed. Numerous examples show how to carry out this program in practice. Even though the statistical content of the handbook has been deliberately limited, it nevertheless discusses several practical matters that are rarely included in more comprehensive treatments, but which are vital for experimental success. Among these are the realities of randomization versus split-plotting, the importance of identifying the experimental unit, and a discussion of replication that argues that it is generally not worth the effort. Readers with some prior statistical exposure -- and statisticians -- may also be surprised to find that p-values do not appear anywhere in the book, and that in fact the authors explicitly argue against their use. Those new to the ideas of Statistical Design of Experiments (DOE)-- or even those who have some familiarity but would like greater insight and simplicity -- should find this handbook an effective way to learn about and apply this powerful technology in their own work.

Module Resources

Non ISBN Literary Resources





Montgomery, Douglas

Design and Analysis of Experiments,

John Wiley & Sons


Box, Hunter & Hunter,

Statistics for Experimenters

John Wiley & Sons


Turner. Charles and Hicks. Kenneth

Fundamental Concepts in the Design of Experiments

Oxford University Press.


Ross, Phillip

Taguchi Techniques for Quality Engineering



Roy, Ranjit

Design of Experiments Using the Taguchi Approach : 16 Steps to Product and Process Improvement,.

John Wiley & Sons


Journal Resources


URL Resources


Other Resources


Additional Information