Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Statistical Engineering PDF full book. Access full book title Statistical Engineering by Stefan H. Steiner. Download full books in PDF and EPUB format.
Author: Stefan H. Steiner Publisher: Quality Press ISBN: 0873891368 Category : Business & Economics Languages : en Pages : 717
Book Description
Reducing the variation in process outputs is a key part of process improvement. For mass produced components and assemblies, reducing variation can simultaneously reduce overall cost, improve function and increase customer satisfaction with the product. The authors have structured this book around an algorithm for reducing process variation that they call "Statistical Engineering." The algorithm is designed to solve chronic problems on existing high to medium volume manufacturing and assembly processes. The fundamental basis for the algorithm is the belief that we will discover cost effective changes to the process that will reduce variation if we increase our knowledge of how and why a process behaves as it does. A key way to increase process knowledge is to learn empirically, that is, to learn by observation and experimentation. The authors discuss in detail a framework for planning and analyzing empirical investigations, known by its acronym QPDAC (Question, Plan, Data, Analysis, Conclusion). They classify all effective ways to reduce variation into seven approaches. A unique aspect of the algorithm forces early consideration of the feasibility of each of the approaches. Also includes case studies, chapter exercises, chapter supplements, and six appendices. PRAISE FOR Statistical Engineering "I found this book uniquely refreshing. Don't let the title fool you. The methods described in this book are statistically sound but require very little statistics. If you have ever wanted to solve a problem with statistical certainty (without being a statistician) then this book is for you. - A reader in Dayton, OH "This is the most comprehensive treatment of variation reduction methods and insights I’ve ever seen."- Gary M. Hazard Tellabs "Throughout the text emphasis has been placed on teamwork, fixing the obvious before jumping to advanced studies, and cost of implementation. All this makes the manuscript !attractive for real-life application of complex techniques." - Guru Chadhabr Comcast IP Services COMMENTS FROM OTHER CUSTOMERS Average Customer Rating (5 of 5 based on 1 review) "This is NOT a typical book on statistical tools. It is a strategy book on how to search for cost-effective changes to reduce variation using empirical means (i.e. observation and experiment). The uniqueness of this book: Summarizes the seven ways to reduce variation so we know the goal of the data gathering and analysis, present analysis results using graphs instead of P-value, and integrates Taguchi, Shainin methods, and classical statistical approach. It is a must read for those who are in the business of reducing variation using data, in particular for the Six Sigma Black Belts and Master Black Belts. Don't forget to read the solutions to exercises and supplementary materials to each chapter on the enclosed CD-ROM." - A. Wong, Canada
Author: Stefan H. Steiner Publisher: Quality Press ISBN: 0873891368 Category : Business & Economics Languages : en Pages : 717
Book Description
Reducing the variation in process outputs is a key part of process improvement. For mass produced components and assemblies, reducing variation can simultaneously reduce overall cost, improve function and increase customer satisfaction with the product. The authors have structured this book around an algorithm for reducing process variation that they call "Statistical Engineering." The algorithm is designed to solve chronic problems on existing high to medium volume manufacturing and assembly processes. The fundamental basis for the algorithm is the belief that we will discover cost effective changes to the process that will reduce variation if we increase our knowledge of how and why a process behaves as it does. A key way to increase process knowledge is to learn empirically, that is, to learn by observation and experimentation. The authors discuss in detail a framework for planning and analyzing empirical investigations, known by its acronym QPDAC (Question, Plan, Data, Analysis, Conclusion). They classify all effective ways to reduce variation into seven approaches. A unique aspect of the algorithm forces early consideration of the feasibility of each of the approaches. Also includes case studies, chapter exercises, chapter supplements, and six appendices. PRAISE FOR Statistical Engineering "I found this book uniquely refreshing. Don't let the title fool you. The methods described in this book are statistically sound but require very little statistics. If you have ever wanted to solve a problem with statistical certainty (without being a statistician) then this book is for you. - A reader in Dayton, OH "This is the most comprehensive treatment of variation reduction methods and insights I’ve ever seen."- Gary M. Hazard Tellabs "Throughout the text emphasis has been placed on teamwork, fixing the obvious before jumping to advanced studies, and cost of implementation. All this makes the manuscript !attractive for real-life application of complex techniques." - Guru Chadhabr Comcast IP Services COMMENTS FROM OTHER CUSTOMERS Average Customer Rating (5 of 5 based on 1 review) "This is NOT a typical book on statistical tools. It is a strategy book on how to search for cost-effective changes to reduce variation using empirical means (i.e. observation and experiment). The uniqueness of this book: Summarizes the seven ways to reduce variation so we know the goal of the data gathering and analysis, present analysis results using graphs instead of P-value, and integrates Taguchi, Shainin methods, and classical statistical approach. It is a must read for those who are in the business of reducing variation using data, in particular for the Six Sigma Black Belts and Master Black Belts. Don't forget to read the solutions to exercises and supplementary materials to each chapter on the enclosed CD-ROM." - A. Wong, Canada
Author: Hartmut Schiefer Publisher: Springer Nature ISBN: 3658323973 Category : Mathematics Languages : en Pages : 139
Book Description
This book describes how statistical methods can be effectively applied in the work of an engineer in terms that can be readily understood. Application of these methods enables the effort involved in experiments to be reduced, the results of these experiments to be fully evaluated, and statistically sound statements to be made as a result. Products can be developed more efficiently and manufactured more cost-effectively, not to mention with greater process reliability. The overarching aim is to save time, money, and materials. From the examples provided, the nature of the practical application can be clearly grasped in each case. This book is a translation of the original German 1st edition Statistik für Ingenieure by Hartmut Schiefer and Felix Schiefer, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2018. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). The present version has been revised technically and linguistically by the authors in collaboration with a professional translator. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.
Author: Kamel Rekab Publisher: CRC Press ISBN: 1574446258 Category : Mathematics Languages : en Pages : 257
Book Description
In today's high-technology world, with flourishing e-business and intense competition at a global level, the search for the competitive advantage has become a crucial task of corporate executives. Quality, formerly considered a secondary expense, is now universally recognized as a necessary tool. Although many statistical methods are available for determining quality, there has been no guide to easy learning and implementation until now. Filling that gap, Statistical Design of Experiments with Engineering Applications, provides a ready made, quick and easy-to-learn approach for applying design of experiments techniques to problems. The book uses quality as the main theme to explain various design of experiments concepts. The authors examine the entire product lifecycle and the tools and techniques necessary to measure quality at each stage. They explain topics such as optimization, Taguchi's method, variance reduction, and graphical applications based on statistical techniques. Wherever applicable the book supplies practical rules of thumb, step-wise procedures that allow you to grasp concepts quickly and apply them appropriately, and examples that demonstrate how to apply techniques. Emphasizing the importance of quality to products and services, the authors include concepts from the field of Quality Engineering. Written with an emphasis on application and not on bogging you down with the theoretical underpinnings, the book enables you to solve 80% of design problems without worrying about the derivation of mathematical formulas.
Author: John W. Daily Publisher: Cambridge University Press ISBN: 1108415318 Category : Mathematics Languages : en Pages : 285
Book Description
Clearly connects macroscopic and microscopic thermodynamics and explains non-equilibrium behavior in kinetic theory and chemical kinetics.
Author: Theodore T. Allen Publisher: Springer Science & Business Media ISBN: 1849960003 Category : Technology & Engineering Languages : en Pages : 572
Book Description
Lean production, has long been regarded as critical to business success in many industries. Over the last ten years, instruction in six sigma has been increasingly linked with learning about the elements of lean production. Introduction to Engineering Statistics and Lean Sigma builds on the success of its first edition (Introduction to Engineering Statistics and Six Sigma) to reflect the growing importance of the "lean sigma" hybrid. As well as providing detailed definitions and case studies of all six sigma methods, Introduction to Engineering Statistics and Lean Sigma forms one of few sources on the relationship between operations research techniques and lean sigma. Readers will be given the information necessary to determine which sigma methods to apply in which situation, and to predict why and when a particular method may not be effective. Methods covered include: • control charts and advanced control charts, • failure mode and effects analysis, • Taguchi methods, • gauge R&R, and • genetic algorithms. The second edition also greatly expands the discussion of Design For Six Sigma (DFSS), which is critical for many organizations that seek to deliver desirable products that work first time. It incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on the design of experiments, and on two level and full factorial experiments, to help improve student intuition-building and retention. The emphasis on lean production, combined with recent methods relating to Design for Six Sigma (DFSS), makes Introduction to Engineering Statistics and Lean Sigma a practical, up-to-date resource for advanced students, educators, and practitioners.
Author: Hoang Pham Publisher: Springer Nature ISBN: 3030769046 Category : Technology & Engineering Languages : en Pages : 497
Book Description
This book presents the state-of-the-art methodology and detailed analytical models and methods used to assess the reliability of complex systems and related applications in statistical reliability engineering. It is a textbook based mainly on the author’s recent research and publications as well as experience of over 30 years in this field. The book covers a wide range of methods and models in reliability, and their applications, including: statistical methods and model selection for machine learning; models for maintenance and software reliability; statistical reliability estimation of complex systems; and statistical reliability analysis of k out of n systems, standby systems and repairable systems. Offering numerous examples and solved problems within each chapter, this comprehensive text provides an introduction to reliability engineering graduate students, a reference for data scientists and reliability engineers, and a thorough guide for researchers and instructors in the field.
Author: Gerald J. Hahn Publisher: Wiley-Interscience ISBN: 9780471040651 Category : Mathematics Languages : en Pages : 0
Book Description
A detailed treatment on the use of statistical models representing physical phenomena. Considers the relevance of the popular normal distribution models and the applicability of exponential distribution in reliability problems. Introduces and discusses the use of alternate models such as gamma, beta and Weibull distributions. Features expansive coverage of system performance and describes an exact method known as the transformation of variables. Deals with techniques on assessing the adequacy of a chosen model including both graphical and analytical procedures. Contains scores of illustrative examples, most of which have been adapted from actual problems.
Author: H. C. Taneja Publisher: I. K. International Pvt Ltd ISBN: 9380026668 Category : Mathematics Languages : en Pages : 365
Book Description
The present book is meant for the first-year students of various universities. Engineering educationists feel that first-year students of all disciplines must have an elementary and general idea about various branches of electronics. Spread in sixteen chapters, the book broadly discusses.
Author: Stefan H. Steiner
Author: Stefan H. Steiner
Author: Hartmut Schiefer
Author: Harrison M. Wadsworth
Author: Kamel Rekab
Author: John W. Daily
Author: Theodore T. Allen
Author: Charles Lipson
Author: Hoang Pham
Author: Gerald J. Hahn
Author: H. C. Taneja