Design of Experiments for Engineers and Scientists PDF Download
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Author: Jiju Antony
Publisher: Elsevier
ISBN: 0443151741
Category : Technology & Engineering
Languages : en
Pages : 296
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Book Description
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
Author: Jiju Antony
Publisher: Elsevier
ISBN: 0443151741
Category : Technology & Engineering
Languages : en
Pages : 296
Get Book Here
Book Description
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
Author: Claes Wohlin
Publisher: Springer Science & Business Media
ISBN: 3642290442
Category : Computers
Languages : en
Pages : 249
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Book Description
Like other sciences and engineering disciplines, software engineering requires a cycle of model building, experimentation, and learning. Experiments are valuable tools for all software engineers who are involved in evaluating and choosing between different methods, techniques, languages and tools. The purpose of Experimentation in Software Engineering is to introduce students, teachers, researchers, and practitioners to empirical studies in software engineering, using controlled experiments. The introduction to experimentation is provided through a process perspective, and the focus is on the steps that we have to go through to perform an experiment. The book is divided into three parts. The first part provides a background of theories and methods used in experimentation. Part II then devotes one chapter to each of the five experiment steps: scoping, planning, execution, analysis, and result presentation. Part III completes the presentation with two examples. Assignments and statistical material are provided in appendixes. Overall the book provides indispensable information regarding empirical studies in particular for experiments, but also for case studies, systematic literature reviews, and surveys. It is a revision of the authors’ book, which was published in 2000. In addition, substantial new material, e.g. concerning systematic literature reviews and case study research, is introduced. The book is self-contained and it is suitable as a course book in undergraduate or graduate studies where the need for empirical studies in software engineering is stressed. Exercises and assignments are included to combine the more theoretical material with practical aspects. Researchers will also benefit from the book, learning more about how to conduct empirical studies, and likewise practitioners may use it as a “cookbook” when evaluating new methods or techniques before implementing them in their organization.
Author: David Sweet
Publisher: Simon and Schuster
ISBN: 1638356904
Category : Computers
Languages : en
Pages : 246
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Book Description
Optimize the performance of your systems with practical experiments used by engineers in the world’s most competitive industries. In Experimentation for Engineers: From A/B testing to Bayesian optimization you will learn how to: Design, run, and analyze an A/B test Break the "feedback loops" caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization Clearly define business metrics used for decision-making Identify and avoid the common pitfalls of experimentation Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You’ll start with a deep dive into methods like A/B testing, and then graduate to advanced techniques used to measure performance in industries such as finance and social media. Learn how to evaluate the changes you make to your system and ensure that your testing doesn’t undermine revenue or other business metrics. By the time you’re done, you’ll be able to seamlessly deploy experiments in production while avoiding common pitfalls. About the technology Does my software really work? Did my changes make things better or worse? Should I trade features for performance? Experimentation is the only way to answer questions like these. This unique book reveals sophisticated experimentation practices developed and proven in the world’s most competitive industries that will help you enhance machine learning systems, software applications, and quantitative trading solutions. About the book Experimentation for Engineers: From A/B testing to Bayesian optimization delivers a toolbox of processes for optimizing software systems. You’ll start by learning the limits of A/B testing, and then graduate to advanced experimentation strategies that take advantage of machine learning and probabilistic methods. The skills you’ll master in this practical guide will help you minimize the costs of experimentation and quickly reveal which approaches and features deliver the best business results. What's inside Design, run, and analyze an A/B test Break the “feedback loops” caused by periodic retraining of ML models Increase experimentation rate with multi-armed bandits Tune multiple parameters experimentally with Bayesian optimization About the reader For ML and software engineers looking to extract the most value from their systems. Examples in Python and NumPy. About the author David Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram. He teaches in the AI and Data Science master's programs at Yeshiva University. Table of Contents 1 Optimizing systems by experiment 2 A/B testing: Evaluating a modification to your system 3 Multi-armed bandits: Maximizing business metrics while experimenting 4 Response surface methodology: Optimizing continuous parameters 5 Contextual bandits: Making targeted decisions 6 Bayesian optimization: Automating experimental optimization 7 Managing business metrics 8 Practical considerations
Author: Natalia Juristo
Publisher: Springer Science & Business Media
ISBN: 1475733046
Category : Computers
Languages : en
Pages : 405
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Book Description
Basics of Software Engineering Experimentation is a practical guide to experimentation in a field which has long been underpinned by suppositions, assumptions, speculations and beliefs. It demonstrates to software engineers how Experimental Design and Analysis can be used to validate their beliefs and ideas. The book does not assume its readers have an in-depth knowledge of mathematics, specifying the conceptual essence of the techniques to use in the design and analysis of experiments and keeping the mathematical calculations clear and simple. Basics of Software Engineering Experimentation is practically oriented and is specially written for software engineers, all the examples being based on real and fictitious software engineering experiments.
Author: William J. Diamond
Publisher: Wiley
ISBN: 9780471289715
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
Author: Nick Arnold
Publisher:
ISBN: 0711243379
Category :
Languages : en
Pages : 83
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Book Description
Science isn't limited to the classroom--it can be cooked up in the kitchen! This photographic book of experiments and projects covers covers chemical reactions, states of matter, microbiology, and much more- all with ingredients and equipment that can be found in the kitchen. The STEAM Ahead series shows readers that science isn't limited to the classroom--it can be found out in the garden, cooked up in the kitchen, and brought to life with paper and paints! Each book features clear, step-by-step instructions and has a fresh, contemporary design, with an emphasis on fun, achievable experiments to give kids hands-on experiences. The science behind each experiment is explained, giving readers the theory behind the practical activities.
Author: Francis J. Hopcroft
Publisher: CRC Press
ISBN: 1000527158
Category : Technology & Engineering
Languages : en
Pages : 338
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Book Description
Experiment Design for Environmental Engineering provides a wide range of practical environmental engineering laboratory experiments for implementation by students in a university laboratory or by practicing professionals in the field, along with an extensive discussion on how to design an experiment that will provide meaningful and useful data, how to interpret the data generated from an experiment, and how to present those data to an audience of other students or professionals. The example experiments provide a way to evaluate a new design against an existing experiment to determine what information is most appropriate in each section and how to format the data for the most effective outcome. Features Fills in the gap in ABET requirements to teach students how to design experiments and includes key elements for a successful design Covers experiments for a wide range of environmental engineering topics Provides standardized approach that includes a basic background to the concepts and step-by-step procedure for conducting the experiment Explains designs that are suitable for college laboratory and professional applications Shows how to organize experimental data as it is collected to optimize usefulness Provides templates for design of the experiment and for presenting the resulting data to technical and nontechnical audiences or clients
Author: Les Kirkup
Publisher: Cambridge University Press
ISBN: 1108418465
Category : Science
Languages : en
Pages : 239
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Book Description
An overview of experimental methods providing practical advice to students seeking guidance with their experimental work.
Author: Alcino J. Silva
Publisher: Oxford University Press
ISBN: 0199329044
Category : Psychology
Languages : en
Pages : 204
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Book Description
Science is growing at a pace that exceeds our comprehension. This is no less true of neuroscience than any other discipline. Ambiguity about what is known and what has been disproven confounds researchers and hampers research planning. There are simply too many research articles and too few hours in the day for anyone to read all that is relevant, let alone distinguish the reliable results from the sketchy ones. Engineering the Next Revolution in Neuroscience explores the proposal that we can overcome these obstacles to scientific progress, and revolutionize neuroscience, by using a framework to map the experimental record. With case studies from learning and memory research, the authors show that we can construct networks of experimental research that make the state of our knowledge manifest. Armed with maps of experiments, scientists can determine more efficiently what their fields have accomplished and where the unexplored territories still reside.
Author: Marco Dorigo
Publisher: MIT Press
ISBN: 9780262041645
Category : Computers
Languages : en
Pages : 238
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Book Description
foreword by Lashon Booker To program an autonomous robot to act reliably in a dynamic environment is a complex task. The dynamics of the environment are unpredictable, and the robots' sensors provide noisy input. A learning autonomous robot, one that can acquire knowledge through interaction with its environment and then adapt its behavior, greatly simplifies the designer's work. A learning robot need not be given all of the details of its environment, and its sensors and actuators need not be finely tuned. Robot Shaping is about designing and building learning autonomous robots. The term "shaping" comes from experimental psychology, where it describes the incremental training of animals. The authors propose a new engineering discipline, "behavior engineering," to provide the methodologies and tools for creating autonomous robots. Their techniques are based on classifier systems, a reinforcement learning architecture originated by John Holland, to which they have added several new ideas, such as "mutespec," classifier system "energy,"and dynamic population size. In the book they present Behavior Analysis and Training (BAT) as an example of a behavior engineering methodology.
