The Tools You Need To Be A Successful Engineer As you read through this new text, you'll discover the importance of Statistical Quality Control (SQC) tools in engineering process monitoring and improvement. You'll learn what SQC methods can and cannot do, and why these are valuable additions to your engineering tool kit. And instead of overwhelming you with unnecessary details, the authors make the implementation of statistical tools "user-friendly." The rich set of examples and problems integrated throughout this book will help you gain a better understanding of where and how to apply SQC tools. Real projects, cases and data sets show you clearly how SQC tools are used in practice. Topics are covered in the right amount of detail to give you insight into their relative importance in modern quality assurance and the ability to immediately use them. This approach provides the mix of tools you'll need to succeed in your engineering career. Key Features of the Text * Provides a coherent presentation of the role of statistics in quality assurance. * Places special attention on making sure that while the technical details are absolutely correct, they do not overwhelm the reader. * Presents the material in realistic contexts, with examples and problems that are based on real-world projects, cases and data sets. * The implementation of statistical tools is user-friendly. * The statistical treatment emphasizes graphics and estimation (and de-emphasizes hypothesis testing).

Reflecting more than 30 years of teaching experience in the field, this guide provides engineers with an introduction to statistics and its applicability to engineering. Examples cover a wide range of engineering applications, including both chemical engineering and semiconductors. Among the topics featured are: quality assurance and statistics, continuous variables, hypothesis testing, comparative experiments, acceptance sampling, the analysis of variance, Taguchi and Orthogonal arrays. Tables, references and an index round out this work.

This comprehensive textbook is a basic reference which should be recommended to students and teachers in engineering, technology and management as well as to the whole community of professionals already working in quality-related areas. The book aims to be a step-by-step introduction to statistical quality assurance. It has been specifically designed for self-study and includes over 100 fully solved exercises and worked examples. In addition to traditional quality control procedures the book also presents very carefully elaborated results of recent research in order to encourage their adoption into practice.

This undergraduate statistical quality assurance textbook clearly shows with real projects, cases and data sets how statistical quality control tools are used in practice. Among the topics covered is a practical evaluation of measurement effectiveness for both continuous and discrete data. Gauge Reproducibility and Repeatability methodology (including confidence intervals for Repeatability, Reproducibility and the Gauge Capability Ratio) is thoroughly developed. Process capability indices and corresponding confidence intervals are also explained. In addition to process monitoring techniques, experimental design and analysis for process improvement are carefully presented. Factorial and Fractional Factorial arrangements of treatments and Response Surface methods are covered. Integrated throughout the book are rich sets of examples and problems that help readers gain a better understanding of where and how to apply statistical quality control tools. These large and realistic problem sets in combination with the streamlined approach of the text and extensive supporting material facilitate reader understanding. Second Edition Improvements Extensive coverage of measurement quality evaluation (in addition to ANOVA Gauge R&R methodologies) New end-of-section exercises and revised-end-of-chapter exercises Two full sets of slides, one with audio to assist student preparation outside-of-class and another appropriate for professors’ lectures Substantial supporting material Supporting Material Seven R programs that support variables and attributes control chart construction and analyses, Gauge R&R methods, analyses of Fractional Factorial studies, Propagation of Error analyses and Response Surface analyses Documentation for the R programs Excel data files associated with the end-of-chapter problem sets, most from real engineering settings

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.

To practice engineering effectively, engineers must need to have a working knowledge of statistical concepts and methods. What they do not need is a background heavy on statistical theory and number crunching. Statistical Methods for Industrial Process Control provides the practical statistics foundation engineers can immediately apply to the work they do every day, regardless of their industry or specialty. The author illustrates statistical concepts and methods with authentic semiconductor manufacturing process examples-integrated circuit fabrication is an exceedingly rich medium for communicating statistical concepts. However, once learned, these concepts and methods can easily be extended and applied to a variety of other industries. The text emphasizes the application of statistical tools, rather than statistical theory. Modern advances in statistical software have made tedious computations and formula memorization unnecessary. Therefore, the author demonstrates software use throughout the book and supplies MINITAB examples and SAS programs. Review problems at the end of each chapter challenge and deepen readers' understanding of the material. Statistical Methods for Industrial Process Control addresses topics that support the work engineers do, rather than educate them as statisticians, and these topics also reflect modern usage. It effectively introduces novice engineers to a fascinating industry and enables experienced engineers to build upon their existing knowledge and learn new skills.

It has recently become apparent that "quality" is quickly becoming the single most important factor for success and growth in business. Companies achieving higher quality in their products through effective quality improvement programs enjoy a significant competitive advantage. It is, therefore, essential for engineers responsible for design, devel

This work details the fundamentals of applied statistics and experimental design, presenting a unified approach to data handling that emphasizes the analysis of variance, regression analysis and the use of Statistical Analysis System computer programs. This edition: discusses modern nonparametric methods; contains information on statistical process control and reliability; supplies fault and event trees; furnishes numerous additional end-of-chapter problems and worked examples; and more.

This book focuses on statistical methods useful in quality control, emphasizing on data-analysis and decision-making. These techniques are also of great use in areas such as laboratory analyses and research. The problems and examples presented are from actual cases encountered in the industry.