This third edition of Design of Experiments for Engineers and Scientists adds to the tried and trusted tools that were successful in so many engineering organizations with new coverage of design of experiments (DoE) in the service sector. Case studies are updated throughout, and new ones are added on dentistry, higher education, and utilities. Although many books have been written on DoE for statisticians, this book overcomes the challenges a wider audience faces in using statistics by using easy-to-read graphical tools. Readers will find the concepts in this book both familiar and easy to understand, and users will soon be able to apply them in their work or research. This classic book is essential reading for engineers and scientists from all disciplines tackling all kinds of product and process quality problems and will be an ideal resource for students of this topic. Written in nonstatistical language, the book is an essential and accessible text for scientists and engineers who want to learn how to use DoE Explains why teaching DoE techniques in the improvement phase of Six Sigma is an important part of problem-solving methodology New edition includes two new chapters on DoE for services as well as case studies illustrating its wider application in the service industry

Why study the theory of experiment design? Although it can be useful to know about special designs for specific purposes, experience suggests that a particular design can rarely be used directly. It needs adaptation to accommodate the circumstances of the experiment. Successful designs depend upon adapting general theoretical principles to the spec

Design of Experiments: A Modern Approach introduces readers to planning and conducting experiments, analyzing the resulting data, and obtaining valid and objective conclusions. This innovative textbook uses design optimization as its design construction approach, focusing on practical experiments in engineering, science, and business rather than orthogonal designs and extensive analysis. Requiring only first-course knowledge of statistics and familiarity with matrix algebra, student-friendly chapters cover the design process for a range of various types of experiments. The text follows a traditional outline for a design of experiments course, beginning with an introduction to the topic, historical notes, a review of fundamental statistics concepts, and a systematic process for designing and conducting experiments. Subsequent chapters cover simple comparative experiments, variance analysis, two-factor factorial experiments, randomized complete block design, response surface methodology, designs for nonlinear models, and more. Readers gain a solid understanding of the role of experimentation in technology commercialization and product realization activities—including new product design, manufacturing process development, and process improvement—as well as many applications of designed experiments in other areas such as marketing, service operations, e-commerce, and general business operations.

"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.

Using engaging prose, Mary E. Harrington introduces neuroscience students to the principles of scientific research including selecting a topic, designing an experiment, analyzing data, and presenting research. This new third edition updates and clarifies the book's wealth of examples while maintaining the clear and effective practical advice of the previous editions. New and expanded topics in this edition include techniques such as optogenetics and conditional transgenes as well as a discussion of rigor and reproducibility in neuroscience research. Extended coverage of descriptive and inferential statistics arms readers with the analytical tools needed to interpret data. Throughout, practical guidelines are provided on avoiding experimental design problems, presenting research including creating posters and giving talks, and using a '12-step guide' to reading scientific journal articles.

Originally published in 2006, the second edition of The Design of Experiments in Neuroscience continues to be an excellent and eminently readable guideline for students beginning their scientific careers. Although all of the examples are specific to neuroscience, this slender volume offers valuable illumination on core practices, principles, and experimental approaches pertinent for all new researchers. Chapter topics cover recognizing pseudoscience, ethics, how to critically read journal articles, how to pick an experimental question, basic research design, controlling variables, and tips for becoming an independent investigator. Each of the eight chapters provides descriptive figures and extra information boxes, questions to check reader comprehension, additional thought questions, further reading suggestions, and Web resources. The six appendixes are as valuable as the main text, including information on working with data, writing research papers, a sample paper, questions and exercises for review, a glossary, and answers to chapter questions. Neuroscientist Harrington (Smith College) has created a wonderful resource that should be a must read for every neuroscientist in training, if not all novice scientists. Summing Up: Highly recommended. Upper-division undergraduates and graduate students. Upper-division Undergraduates; Graduate Students. Reviewed by C. L. Iwema.