Statistical Methods for Quality Improvement
Statistical Methods for Quality Improvement

Author: Thomas P. Ryan

Publisher: John Wiley & Sons

Published: 2011-09-20

Total Pages: 578

ISBN-13: 1118058100

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Praise for the Second Edition "As a comprehensive statistics reference book for quality improvement, it certainly is one of the best books available." —Technometrics This new edition continues to provide the most current, proven statistical methods for quality control and quality improvement The use of quantitative methods offers numerous benefits in the fields of industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems. Statistical Methods for Quality Improvement, Third Edition guides readers through a broad range of tools and techniques that make it possible to quickly identify and resolve both current and potential trouble spots within almost any manufacturing or nonmanufacturing process. The book provides detailed coverage of the application of control charts, while also exploring critical topics such as regression, design of experiments, and Taguchi methods. In this new edition, the author continues to explain how to combine the many statistical methods explored in the book in order to optimize quality control and improvement. The book has been thoroughly revised and updated to reflect the latest research and practices in statistical methods and quality control, and new features include: Updated coverage of control charts, with newly added tools The latest research on the monitoring of linear profiles and other types of profiles Sections on generalized likelihood ratio charts and the effects of parameter estimation on the properties of CUSUM and EWMA procedures New discussions on design of experiments that include conditional effects and fraction of design space plots New material on Lean Six Sigma and Six Sigma programs and training Incorporating the latest software applications, the author has added coverage on how to use Minitab software to obtain probability limits for attribute charts. new exercises have been added throughout the book, allowing readers to put the latest statistical methods into practice. Updated references are also provided, shedding light on the current literature and providing resources for further study of the topic. Statistical Methods for Quality Improvement, Third Edition is an excellent book for courses on quality control and design of experiments at the upper-undergraduate and graduate levels. the book also serves as a valuable reference for practicing statisticians, engineers, and physical scientists interested in statistical quality improvement.

Measuring Data Quality for Ongoing Improvement
Measuring Data Quality for Ongoing Improvement

Author: Laura Sebastian-Coleman

Publisher: Newnes

Published: 2012-12-31

Total Pages: 404

ISBN-13: 0123977541

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The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You'll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You'll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies. - Demonstrates how to leverage a technology independent data quality measurement framework for your specific business priorities and data quality challenges - Enables discussions between business and IT with a non-technical vocabulary for data quality measurement - Describes how to measure data quality on an ongoing basis with generic measurement types that can be applied to any situation

Acceptance Sampling in Quality Control
Acceptance Sampling in Quality Control

Author: Edward G. Schilling

Publisher: CRC Press

Published: 2017-06-01

Total Pages: 873

ISBN-13: 1351647075

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Acceptance Sampling in Quality Control, Third Edition presents the state of the art in the methodology of sampling while integrating both theory and best practices. It discusses various standards, including those from the ISO, MIL-STD and ASTM and explores how to set quality levels. The book also includes problems at the end of each chapter with solutions. This edition improves upon the previous editions especially in the areas of software applications and compliance sampling plans. New to the Third Edition: Numerous Microsoft Excel templates to address sampling plans are used. Commercial software applications are discussed at the end of many chapters. Discussion of quick switching systems has been expanded to account for the considerable recent activity in this area. Added discussion of zero acceptance number chained quick switching systems.

Quality Engineering Statistics
Quality Engineering Statistics

Author: Robert A. Dovich

Publisher: ASQ Quality Press

Published: 1992

Total Pages: 0

ISBN-13: 9780873891417

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This book is a compendium of many of the statistical tools and tests used by quality and engineering professionals. it is a practical handbook that lists significant statistical methods, outlines assumptions for testing, and provides formulas and completed examples. the book is ideal for engineers who know what type of test to perform but need an easy-to-use reference to help complete the task. Readers should have an understanding of basic statistics. a bibliography contains references to texts that can provide the necessary theory and mathematical foundations.

Introduction to Engineering Statistics and Lean Sigma
Introduction to Engineering Statistics and Lean Sigma

Author: Theodore T. Allen

Publisher: Springer Science & Business Media

Published: 2010-04-23

Total Pages: 573

ISBN-13: 1849960003

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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.

Statistical Methods in Water Resources
Statistical Methods in Water Resources

Author: D.R. Helsel

Publisher: Elsevier

Published: 1993-03-03

Total Pages: 539

ISBN-13: 0080875084

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Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.

Multivariate Statistical Quality Control Using R
Multivariate Statistical Quality Control Using R

Author: Edgar Santos-Fernández

Publisher: Springer Science & Business Media

Published: 2012-09-22

Total Pages: 134

ISBN-13: 1461454530

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​​​​​The intensive use of automatic data acquisition system and the use of cloud computing for process monitoring have led to an increased occurrence of industrial processes that utilize statistical process control and capability analysis. These analyses are performed almost exclusively with multivariate methodologies. The aim of this Brief is to present the most important MSQC techniques developed in R language. The book is divided into two parts. The first part contains the basic R elements, an introduction to statistical procedures, and the main aspects related to Statistical Quality Control (SQC). The second part covers the construction of multivariate control charts, the calculation of Multivariate Capability Indices.

Applied Statistics Manual
Applied Statistics Manual

Author: Matthew A. Barsalou

Publisher: Quality Press

Published: 2018-12-19

Total Pages: 371

ISBN-13: 087389975X

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This book was written to provide guidance for those who need to apply statistical methods for practical use. While the book provides detailed guidance on the use of Minitab for calculation, simply entering data into a software program is not sufficient to reliably gain knowledge from data. The software will provide an answer, but the answer may be wrong if the sample was not taken properly, the data was unsuitable for the statistical test that was performed, or the wrong test was selected. It is also possible that the answer will be correct, but misinterpreted. This book provides both guidance in applying the statistical methods described as well as instructions for performing calculations without a statistical software program such as Minitab. One of the authors is a professional statistician who spent nearly 13 years working at Minitab and the other is an experienced and certified Lean Six Sigma Master Black Belt. Together, they strive to present the knowledge of a statistician in a format that can be easily understood and applied by non-statisticians facing real-world problems. Their guidance is provided with the goal of making data analysis accessible and practical. Rather than focusing on theoretical concepts, the book delivers only the information that is critical to success for the practitioner. It is a thorough guide for those who have not yet been exposed to the value of statistics, as well as a reliable reference for those who have been introduced to statistics but are not yet confident in their abilities.

Statistics for the Quality Control Chemistry Laboratory
Statistics for the Quality Control Chemistry Laboratory

Author: Eamonn Mullins

Publisher: Royal Society of Chemistry

Published: 2007-10-31

Total Pages: 475

ISBN-13: 1847551939

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Statistical methods are essential tools for analysts, particularly those working in Quality Control Laboratories. This book provides a sound introduction to their use in analytical chemistry, without requiring a strong mathematical background. It emphasises simple graphical methods of data analysis, such as control charts, which are also a fundamental requirement in laboratory accreditation. A large part of the book is concerned with the design and analysis of laboratory experiments, including sample size determination. Practical case studies and many real databases from both QC laboratories and the research literature, are used to illustrate the ideas in action. The aim of Statistics for the Quality Control Chemistry Laboratory is to give the reader a strong grasp of the concept of statistical variation in laboratory data and of the value of simple statistical ideas ad methods in thinking about and manipulation such data, It will be invaluable to analysts working in QC laboratories in industry, hospitals and public health, and will also be welcomed as a textbook for aspiring analysts in colleges and universities.

Federal Statistics, Multiple Data Sources, and Privacy Protection
Federal Statistics, Multiple Data Sources, and Privacy Protection

Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

Published: 2018-01-27

Total Pages: 195

ISBN-13: 0309465370

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The environment for obtaining information and providing statistical data for policy makers and the public has changed significantly in the past decade, raising questions about the fundamental survey paradigm that underlies federal statistics. New data sources provide opportunities to develop a new paradigm that can improve timeliness, geographic or subpopulation detail, and statistical efficiency. It also has the potential to reduce the costs of producing federal statistics. The panel's first report described federal statistical agencies' current paradigm, which relies heavily on sample surveys for producing national statistics, and challenges agencies are facing; the legal frameworks and mechanisms for protecting the privacy and confidentiality of statistical data and for providing researchers access to data, and challenges to those frameworks and mechanisms; and statistical agencies access to alternative sources of data. The panel recommended a new approach for federal statistical programs that would combine diverse data sources from government and private sector sources and the creation of a new entity that would provide the foundational elements needed for this new approach, including legal authority to access data and protect privacy. This second of the panel's two reports builds on the analysis, conclusions, and recommendations in the first one. This report assesses alternative methods for implementing a new approach that would combine diverse data sources from government and private sector sources, including describing statistical models for combining data from multiple sources; examining statistical and computer science approaches that foster privacy protections; evaluating frameworks for assessing the quality and utility of alternative data sources; and various models for implementing the recommended new entity. Together, the two reports offer ideas and recommendations to help federal statistical agencies examine and evaluate data from alternative sources and then combine them as appropriate to provide the country with more timely, actionable, and useful information for policy makers, businesses, and individuals.