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Statistics for Experimenters

Statistics for Experimenters Author
ISBN-10 OCLC:780374763
Release 2009
Pages 664
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Statistics for Experimenters has been writing in one form or another for most of life. You can find so many inspiration from Statistics for Experimenters also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Statistics for Experimenters book for free.



Statistics for Experimenters

Statistics for Experimenters Author George E. P. Box
ISBN-10 0470570911
Release 2009-07-14
Pages 664
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This set contains Statistics for Experimenters: Design, Innovation, and Discovery, Second Edition by George E.P. Box, J. Stuart Hunter, and William G. Hunter (978-0-471-71813-0) and JMP(r) Version 6 Software.



Design of Experiments for Engineers and Scientists

Design of Experiments for Engineers and Scientists Author Jiju Antony
ISBN-10 9780080994192
Release 2014-02-22
Pages 220
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The tools and techniques used in Design of Experiments (DoE) have been proven successful in meeting the challenge of continuous improvement in many manufacturing organisations over the last two decades. However research has shown that application of this powerful technique in many companies is limited due to a lack of statistical knowledge required for its effective implementation. Although many books have been written on this subject, they are mainly by statisticians, for statisticians and not appropriate for engineers. Design of Experiments for Engineers and Scientists overcomes the problem of statistics by taking a unique approach using graphical tools. The same outcomes and conclusions are reached as through using statistical methods and readers will find the concepts in this book both familiar and easy to understand. This new edition includes a chapter on the role of DoE within Six Sigma methodology and also shows through the use of simple case studies its importance in the service industry. It is essential reading for engineers and scientists from all disciplines tackling all kinds of manufacturing, product and process quality problems and will be an ideal resource for students of this topic. Written in non-statistical 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 a full chapter on DoE for services as well as case studies illustrating its wider application in the service industry



Statistical Design

Statistical Design Author George Casella
ISBN-10 9780387759647
Release 2008-04-03
Pages 307
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Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block, to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks. George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in many aspects of statistics, having contributed to theoretical statistics in the areas of decision theory and statistical confidence, to environmental statistics, and has more recently concentrated efforts in statistical genomics. He also maintains active research interests in the theory and application of Monte Carlo and other computationally intensive methods. He is listed as an ISI "Highly Cited Researcher." In other capacities, Professor Casella has served as Theory and Methods Editor of the Journal of the American Statistical Association, 1996-1999, Executive Editor of Statistical Science, 2001-2004, and Co-Editor of the Journal of the Royal Statistical Society, Series B, 2009-2012. He has served on the Board of Mathematical Sciences of the National Research Council, 1999-2003, and many committees of both the American Statistical Association and the Institute of Mathematical Statistics. Professor Casella has co-authored five textbooks: Variance Components, 1992; Theory of Point Estimation, Second Edition, 1998; Monte Carlo Statistical Methods, Second Edition, 2004; Statistical Inference, Second Edition, 2001, and Statistical Genomics of Complex Traits, 2007.



Experiments

Experiments Author C. F. Jeff Wu
ISBN-10 9781118211533
Release 2011-09-20
Pages 760
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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 discoveries—and sheds further light on existing ones—on 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.



Statistical Control by Monitoring and Adjustment

Statistical Control by Monitoring and Adjustment Author George E. P. Box
ISBN-10 9781118164464
Release 2011-09-09
Pages 360
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Praise for the First Edition "This book . . . is a significant addition to the literature on statistical practice . . . should be of considerable interest to those interested in these topics."—International Journal of Forecasting Recent research has shown that monitoring techniques alone are inadequate for modern Statistical Process Control (SPC), and there exists a need for these techniques to be augmented by methods that indicate when occasional process adjustment is necessary. Statistical Control by Monitoring and Adjustment, Second Edition presents the relationship among these concepts and elementary ideas from Engineering Process Control (EPC), demonstrating how the powerful synergistic association between SPC and EPC can solve numerous problems that are frequently encountered in process monitoring and adjustment. The book begins with a discussion of SPC as it was originally conceived by Dr. Walter A. Shewhart and Dr. W. Edwards Deming. Subsequent chapters outline the basics of the new integration of SPC and EPC, which is not available in other related books. Thorough coverage of time series analysis for forecasting, process dynamics, and non-stationary models is also provided, and these sections have been carefully written so as to require only an elementary understanding of mathematics. Extensive graphical explanations and computational tables accompany the numerous examples that are provided throughout each chapter, and a helpful selection of problems and solutions further facilitates understanding. Statistical Control by Monitoring and Adjustment, Second Edition is an excellent book for courses on applied statistics and industrial engineering at the upper-undergraduate and graduate levels. It also serves as a valuable reference for statisticians and quality control practitioners working in industry.



Statistical Methods for Six Sigma

Statistical Methods for Six Sigma Author Anand M. Joglekar
ISBN-10 0471465372
Release 2003-09-19
Pages 344
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A guide to achieving business successes through statistical methods Statistical methods are a key ingredient in providing data-based guidance to research and development as well as to manufacturing. Understanding the concepts and specific steps involved in each statistical method is critical for achieving consistent and on-target performance. Written by a recognized educator in the field, Statistical Methods for Six Sigma: In R&D and Manufacturing is specifically geared to engineers, scientists, technical managers, and other technical professionals in industry. Emphasizing practical learning, applications, and performance improvement, Dr. Joglekar?s text shows today?s industry professionals how to: Summarize and interpret data to make decisions Determine the amount of data to collect Compare product and process designs Build equations relating inputs and outputs Establish specifications and validate processes Reduce risk and cost-of-process control Quantify and reduce economic loss due to variability Estimate process capability and plan process improvements Identify key causes and their contributions to variability Analyze and improve measurement systems This long-awaited guide for students and professionals in research, development, quality, and manufacturing does not presume any prior knowledge of statistics. It covers a large number of useful statistical methods compactly, in a language and depth necessary to make successful applications. Statistical methods in this book include: variance components analysis, variance transmission analysis, risk-based control charts, capability and performance indices, quality planning, regression analysis, comparative experiments, descriptive statistics, sample size determination, confidence intervals, tolerance intervals, and measurement systems analysis. The book also contains a wealth of case studies and examples, and features a unique test to evaluate the reader?s understanding of the subject.



Modern Experimental Design

Modern Experimental Design Author Thomas P. Ryan
ISBN-10 9780471210771
Release 2007-02-02
Pages 593
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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.



Optimal Design of Experiments

Optimal Design of Experiments Author Peter Goos
ISBN-10 9781119976165
Release 2011-06-28
Pages 304
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"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.



Experiments with Mixtures

Experiments with Mixtures Author John A. Cornell
ISBN-10 9781118150498
Release 2011-09-20
Pages 680
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The most comprehensive, single-volume guide to conducting experiments with mixtures "If one is involved, or heavily interested, in experiments on mixtures of ingredients, one must obtain this book. It is, as was the first edition, the definitive work." -Short Book Reviews (Publication of the International Statistical Institute) "The text contains many examples with worked solutions and with its extensive coverage of the subject matter will prove invaluable to those in the industrial and educational sectors whose work involves the design and analysis of mixture experiments." -Journal of the Royal Statistical Society "The author has done a great job in presenting the vital information on experiments with mixtures in a lucid and readable style. . . . A very informative, interesting, and useful book on an important statistical topic." -Zentralblatt fur Mathematik und Ihre Grenzgebiete Experiments with Mixtures shows researchers and students how to design and set up mixture experiments, then analyze the data and draw inferences from the results. Virtually every technique that has appeared in the literature of mixtures can be found here, and computing formulas for each method are provided with completely worked examples. Almost all of the numerical examples are taken from real experiments. Coverage begins with Scheffe lattice designs, introducing the use of independent variables, and ends with the most current methods. New material includes: * Multiple response cases * Residuals and least-squares estimates * Categories of components: Mixtures of mixtures * Fixed as well as variable values for the major component proportions * Leverage and the Hat Matrix * Fitting a slack-variable model * Estimating components of variances in a mixed model using ANOVA table entries * Clarification of blocking mates and choice of mates * Optimizing several responses simultaneously * Biplots for multiple responses



Response Surfaces Mixtures and Ridge Analyses

Response Surfaces  Mixtures  and Ridge Analyses Author George E. P. Box
ISBN-10 9780470072752
Release 2007-01-22
Pages 768
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The authority on building empirical models and the fitting of such surfaces to data—completely updated and revised Revising and updating a volume that represents the essential source on building empirical models, George Box and Norman Draper—renowned authorities in this field—continue to set the standard with the Second Edition of Response Surfaces, Mixtures, and Ridge Analyses, providing timely new techniques, new exercises, and expanded material. A comprehensive introduction to building empirical models, this book presents the general philosophy and computational details of a number of important topics, including factorial designs at two levels; fitting first and second-order models; adequacy of estimation and the use of transformation; and occurrence and elucidation of ridge systems. Substantially rewritten, the Second Edition reflects the emergence of ridge analysis of second-order response surfaces as a very practical tool that can be easily applied in a variety of circumstances. This unique, fully developed coverage of ridge analysis—a technique for exploring quadratic response surfaces including surfaces in the space of mixture ingredients and/or subject to linear restrictions—includes MINITAB® routines for performing the calculations for any number of dimensions. Many additional figures are included in the new edition, and new exercises (many based on data from published papers) offer insight into the methods used. The exercises and their solutions provide a variety of supplementary examples of response surface use, forming an extremely important component of the text. Response Surfaces, Mixtures, and Ridge Analyses, Second Edition presents material in a logical and understandable arrangement and includes six new chapters covering an up-to-date presentation of standard ridge analysis (without restrictions); design and analysis of mixtures experiments; ridge analysis methods when there are linear restrictions in the experimental space including the mixtures experiments case, with or without further linear restrictions; and canonical reduction of second-order response surfaces in the foregoing general case. Additional features in the new edition include: New exercises with worked answers added throughout An extensive revision of Chapter 5: Blocking and Fractionating 2k Designs Additional discussion on the projection of two-level designs into lower dimensional spaces This is an ideal reference for researchers as well as a primary text for Response Surface Methodology graduate-level courses and a supplementary text for Design of Experiments courses at the upper-undergraduate and beginning-graduate levels.



Testing 1 2 3

Testing 1 2 3 Author Johannes Ledolter
ISBN-10 0804756120
Release 2007
Pages 300
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This book gives students, practitioners, and managers a set of practical and valuable tools for designing and analyzing experiments, emphasizing applications in marketing and service operations such as website design, direct mail campaigns, and in-store tests.



Improving Almost Anything

Improving Almost Anything Author George E. P. Box
ISBN-10 0471727555
Release 2006-04-21
Pages 598
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Masterworks in process improvement and quality technology-- by George Box and friends George Box has a unique ability to explain complex ideas simply and eloquently. This revised edition of his masterworks since 1982 clearly demonstrates the range of his wit and intellect. These fascinating readings represent the cornerstones in the theory and application of process improvement, product design, and process control. Readers will gain valuable insights into the fundamentals and philosophy of scientific method using statistics and how it can drive creativity and discovery. The book is divided into five key parts: * Part A, Some Thoughts on Quality Improvement, concerns the democratization of the scientific method and, in such papers as "When Murphy Speaks--Listen," advises managers to view operation of their processes as ongoing opportunities for improvement. * Part B, Design of Experiments for Process Improvement, illustrates the enormous advantages offered by experimental design in the pursuit of better products and processes. * Part C, Sequential Investigation and Discovery, shows how sequential assembly of designs allows the experimenter to match the difficulty of the problem with the effort needed to solve it. * Part D, Control, describes application of feedback control in the Statistical Process Control (SPC) environment. A simple graphical technique using Box-Jenkins charts is set forth to appropriately adjust processes to target. * Part E, Variance Reduction and Robustness, demonstrates how the existence of more than one source of variation may be used to achieve products robust to the environment in which they must function and emphasizes the importance of error transmission and data transformation in producing robust assemblies. A Foreword by Dr. J. Stuart Hunter allows readers to gain insight into the workings of a remarkable mind and explains how these ideas can greatly catalyze their efforts in process improvement.



The Statistical Sleuth A Course in Methods of Data Analysis

The Statistical Sleuth  A Course in Methods of Data Analysis Author Fred Ramsey
ISBN-10 9781133490678
Release 2012-05-02
Pages 784
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THE STATISTICAL SLEUTH: A COURSE IN METHODS OF DATA ANALYSIS, Third Edition offers an appealing treatment of general statistical methods that takes full advantage of the computer, both as a computational and an analytical tool. The material is independent of any specific software package, and prominently treats modeling and interpretation in a way that goes beyond routine patterns. The book focuses on a serious analysis of real case studies, strategies and tools of modern statistical data analysis, the interplay of statistics and scientific learning, and the communication of results. With interesting examples, real data, and a variety of exercise types (conceptual, computational, and data problems), the authors get students excited about statistics. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.



A First Course in Design and Analysis of Experiments

A First Course in Design and Analysis of Experiments Author Gary W. Oehlert
ISBN-10 0716735105
Release 2000-01-19
Pages 600
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Oehlert's text is suitable for either a service course for non-statistics graduate students or for statistics majors. Unlike most texts for the one-term grad/upper level course on experimental design, Oehlert's new book offers a superb balance of both analysis and design, presenting three practical themes to students: • when to use various designs • how to analyze the results • how to recognize various design options Also, unlike other older texts, the book is fully oriented toward the use of statistical software in analyzing experiments.



A Doe Handbook

A Doe Handbook Author Bert Gunter
ISBN-10 1497511909
Release 2014-05-15
Pages 118
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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.



Empirical model building and response surfaces

Empirical model building and response surfaces Author George E. P. Box
ISBN-10 MINN:31951D00096163F
Release 1987-01-16
Pages 669
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An innovative discussion of building empirical models and the fitting of surfaces to data. Introduces the general philosophy of response surface methodology, and details least squares for response surface work, factorial designs at two levels, fitting second-order models, adequacy of estimation and the use of transformation, occurrence and elucidation of ridge systems, and more. Some results are presented for the first time. Includes real-life exercises, nearly all with solutions.