QLTY09032 2019 Advanced Experimental Design

General Details

Full Title
Advanced Experimental Design
Transcript Title
Advanced Experimental Design
N/A %
Subject Area
QLTY - Quality
MEMA - Mech and Manufact Eng
09 - NFQ Level 9
05 - 05 Credits
Start Term
2019 - Full Academic Year 2019-20
End Term
9999 - The End of Time
JOHN DONOVAN, Xavier Velay
Programme Membership
SG_SPROJ_M09 201900 Master of Science in Science in Project Management SG_EQLTY_M09 201900 Master of Science in Engineering in Quality SG_SPROJ_O09 201900 Postgraduate Diploma in Science in Project Management

The student will be learn how to design conduct and analyse standard and complex experiments, and interpret the data from these experiments.

Learning Outcomes

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


Conduct two and three level fractional factorial experiments and analyse the resulting data.


Plan, conduct and analyse experiments using Response Surface Methodology (RSM).


Design and analyse mixed level experiments.


Analyse multiple response experiments and interpret the results.


Analyse and interpret data from experiments involving random effects models.


Formulate the expected means square rules to develop appropriate statistical models.


Use Minitab to design an experiment, analyse, interpret and evaluate the resulting data.

Teaching and Learning Strategies


Module Assessment Strategies

Continuous Assessment

Project work - Design and analyse own experiment                                                                       20%.

Final Examination                 

One written paper of 2.5 hours duration on experimental design theory and analysis                    40%

One computer based exam of 2.5 hours duration involving data analysis and interpretation          40%

Repeat Assessments


Indicative Syllabus

1.     Two Level Factorial Designs :  full factorial Designs. fractional factorial designs. Analysis of Residuals. Blocking in two level designs. Fold-over designs. repeat experiments vs. replicate experiments. Building the regression model and verification of the model.

 2.     More complex Two Level Factorial designs : Analysis of single replicate designs using probability plots and Lenth's method. Data transformations in a factorial design, variance stabilisation and Bob-Cox transformations. Analysis of multiple response experiments e.g. mean response and variability of response. Addition of centre points to a design. Plackett-Burman designs, Definitive Screening Designs (DSD), Robust methods of experimental design such as Taguchi methods. Solution of static and dynamic problems using Taguchi methods.

 3.     Three Level Factorial Designs:  Full Factorial Designs and fractional factorial designs,

 4.     Design and Analysis of Mixed Level Designs: Constructing mixed level designs using method of replacement. Analysis strategies for mixed level designs.

 5.     Response Surface Methods: Method of Steepest Ascent, verifying adequacy of first order model. Analysis of second order response surface. Locating the stationary point. Characterising the response surface. Ridge systems. Multiple response problem. Selecting designs for fitting response surfaces. Central composite designs. Box-Behnken designs. Face centred cube design. Blocking in Response surface designs.

 6.     Experiments with Random factors: The random effects model. Rules for Expected means squares. Repeatability and Reproducibility (R&R) studies using the random effects model. Estimation of variance components. Mixed models. Approximate F tests and Satterthwaite's method.

7.     Misc. Designs: Crossed vs. Nested designs. D-optimal designs. Identify hard-to-change and easy-to-change factors that lead to split plot designs.

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 Project Design and analyse own experiment Continuous Assessment UNKNOWN 20 % OnGoing 1,2,7

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Final Exam One final exam 2.5 hour theory paper. One computer based 2.5 hour exam involving data analysis and interpretation Final Exam UNKNOWN 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 3 Weekly 3.00
Independent Learning UNKNOWN Independent Learning 4 Weekly 4.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Part Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Distance Learning Suite Lecture 1 Weekly 1.00
Independent Learning UNKNOWN Independent Learning 4 Weekly 4.00
Total Part Time Average Weekly Learner Contact Time 1.00 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
2016-02-01 Response Surface Methodology John Wiley & Sons
ISBN 9781118916018 ISBN-13 1118916018

Praise for the Third Edition: This new third edition has been substantially rewritten and updated with new topics and material, new examples and exercises, and to more fully illustrate modern applications of RSM. em style="font-weight: bold;"Zentralblatt Math Featuring a substantial revision, the Fourth Edition of Response Surface Methodology: Process and Product Optimization Using Designed Experiments presents updated coverage on the underlying theory and applications of response surface methodology (RSM). Providing the assumptions and conditions necessary to successfully apply RSM in modern applications, the new edition covers classical and modern response surface designs in order to present a clear connection between the designs and analyses in RSM. With multiple revised sections with new topics and expanded coverage, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Fourth Edition includes: Many updates on topics such as optimal designs, optimization techniques, robust parameter design, methods for design evaluation, computer-generated designs, multiple response optimization, and non-normal responses Additional coverage on topics such as experiments with computer models, definitive screening designs, and data measured with error Expanded integration of examples and experiments, which present up-to-date software applications, such as JMP, SAS, and Design-Expert, throughout An extensive references section to help readers stay up-to-date with leading research in the field of RSM An ideal textbook for upper-undergraduate and graduate-level courses in statistics, engineering, and chemical/physical sciences, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Fourth Edition is also a useful reference for applied statisticians and engineers in disciplines such as quality, process, and chemistry. Raymond H. Myers, PhD, is Professor Emeritus in the Department of Statistics at Virginia Polytechnic Institute and State University. He has more than 40 years of academic experience in the areas of experimental design and analysis, response surface analysis, and designs for nonlinear models. A Fellow of the American Statistical Association (ASA) and the American Society for Quality (ASQ), Dr. Myers has authored numerous journal articles and books, including Generalized Linear Models: with Applications in Engineering and the Sciences, Second Edition, also published by Wiley. Douglas C. Montgomery, PhD, is Regents' Professor of Industrial Engineering and Arizona State University Foundation Professor of Engineering. Dr. Montgomery has more than 30 years of academic and consulting experience and his research interest includes the design and analysis of experiments. He is a Fellow of ASA and the Institute of Industrial Engineers, and an Honorary Member of ASQ. He has authored numerous journal articles and books, including Design and Analysis of Experiments, Eighth Edition; Generalized Linear Models: with Applications in Engineering and the Sciences, Second Edition; Introduction to Introduction to Linear Regression Analysis, Fifth Edition; and Introduction to Time Series Analysis and Forecasting, Second Edition, all published by Wiley. Christine M. Anderson-Cook, PhD, is a Research Scientist and Project Leader in the Statistical Sciences Group at the Los Alamos National Laboratory, New Mexico. Dr. Anderson-Cook has over 20 years of academic and consulting experience, and has written numerous journal articles on the topics of design of experiments, response surface methodology and reliability. She is a Fellow of the ASA and ASQ.

Required Reading
2007-02-02 Modern Experimental Design John Wiley & Sons
ISBN 9780471210771 ISBN-13 0471210773

A complete and well-balanced introduction to modern experimentaldesign Using current research and discussion of the topic along withclear applications, Modern Experimental Design highlightsthe guiding role of statistical principles in experimental designconstruction. This text can serve as both an applied introductionas well as a concise review of the essential types of experimentaldesigns and their applications. Topical coverage includes designs containing one or multiplefactors, designs with at least one blocking factor, split-unitdesigns and their variations as well as supersaturated andPlackett-Burman designs. In addition, the text contains extensivetreatment of: Conditional effects analysis as a proposed general method ofanalysis Multiresponse optimization Space-filling designs, including Latin hypercube and uniformdesigns Restricted regions of operability and debarredobservations Analysis of Means (ANOM) used to analyze data from varioustypes of designs The application of available software, including Design-Expert,JMP, and MINITAB This text provides thorough coverage of the topic while alsointroducing the reader to new approaches. Using a large number ofreferences with detailed analyses of datasets, ModernExperimental Design works as a well-rounded learning tool forbeginners as well as a valuable resource for practitioners.

Required Reading
2009-08-10 Experiments Wiley
ISBN 0471699462 ISBN-13 9780471699460

Praise for the First Edition: "If you . . . want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library." Journal of the American Statistical Association Fully updated to reflect the major progress in the use of statistically designed experiments for product and process improvement, Experiments, Second Edition introduces some of the newest discoveriesand sheds further light on existing oneson the design and analysis of experiments and their applications in system optimization, robustness, and treatment comparison. Maintaining the same easy-to-follow style as the previous edition while also including modern updates, this book continues to present a new and integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, reliability improvement, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Along with a new chapter that focuses on regression analysis, the Second Edition features expanded and new coverage of additional topics, including: Expected mean squares and sample size determination One-way and two-way ANOVA with random effects Split-plot designs ANOVA treatment of factorial effects Response surface modeling for related factors Drawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study. Experiments, Second Edition is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians.

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
2011-08-15 Optimal Design of Experiments Wiley
ISBN 0470744618 ISBN-13 9780470744611

"This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book." - Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University "It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion by showing how tailor-made, optimal designs can be effectively employed to meet a client's actual needs. It should be required reading for anyone interested in using the design of experiments in industrial settings." Christopher J. Nachtsheim, Frank A Donaldson Chair in Operations Management, Carlson School of Management, University of Minnesota This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate? What can I do if I have day-to-day variability and I can only perform 3 runs a day? How can I do RSM cost effectively if I have categorical factors? How can I design and analyze experiments when there is a factor that can only be changed a few times over the study? How can I include both ingredients in a mixture and processing factors in the same study? How can I design an experiment if there are many factor combinations that are impossible to run? How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study? How can I take into account batch information in when designing experiments involving multiple batches? How can I add runs to a botched experiment to resolve ambiguities? While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain.

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