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<p/><br></br><p><b> About the Book </b></p></br></br>"Industrial Statistics is concerned with maintaining and improving the quality of goods and services. It involves a broad range of statistical tools but maintaining and improving quality is its main concern. Variability is inherent in all processes, whether they be manufacturing processes or service processes. This variability must be controlled to create high quality goods and services and must be reduced to improve quality. Industrial Statistics focuses on the use of statistical thinking, i.e., the appreciation of the inherent variability of all processes in order that all possible outcomes can be assessed. It also focuses on developing skills for modeling data and designing experiments that can lead to improvements in performance and reductions in variablity"--<p/><br></br><p><b> Book Synopsis </b></p></br></br>Modern Industrial Statistics <p><b>The new edition of the prime reference on the tools of statistics used in industry and services, integrating theoretical, practical, and computer-based approaches</b><p><i>Modern Industrial Statistics</i> is a leading reference and guide to the statistics tools widely used in industry and services. Designed to help professionals and students easily access relevant theoretical and practical information in a single volume, this standard resource employs a computer-intensive approach to industrial statistics and provides numerous examples and procedures in the popular R language and for MINITAB and JMP statistical analysis software. Divided into two parts, the text covers the principles of statistical thinking and analysis, bootstrapping, predictive analytics, Bayesian inference, time series analysis, acceptance sampling, statistical process control, design and analysis of experiments, simulation and computer experiments, and reliability and survival analysis. Part A, on computer age statistical analysis, can be used in general courses on analytics and statistics. Part B is focused on industrial statistics applications.<p>The fully revised third edition covers the latest techniques in R, MINITAB and JMP, and features brand-new coverage of time series analysis, predictive analytics and Bayesian inference. New and expanded simulation activities, examples, and case studies--drawn from the electronics, metal work, pharmaceutical, and financial industries--are complemented by additional computer and modeling methods. Helping readers develop skills for modeling data and designing experiments, this comprehensive volume: <ul><li>Explains the use of computer-based methods such as bootstrapping and data visualization</li><li>Covers nonstandard techniques and applications of industrial statistical process control (SPC) charts</li><li>Contains numerous problems, exercises, and data sets representing real-life case studies of statistical work in various business and industry settings</li><li>Includes access to a companion website that contains an introduction to R, sample R code, csv files of all data sets, JMP add-ins, and downloadable appendices</li><li>Provides an author-created R package, <i>mistat</i>, that includes all data sets and statistical analysis applications used in the book</li></ul><p>Part of the acclaimed <i>Statistics in Practice</i> series, <i>Modern Industrial Statistics with Applications in R, MINITAB, and JMP, Third Edition</i>, is the perfect textbook for advanced undergraduate and postgraduate courses in the areas of industrial statistics, quality and reliability engineering, and an important reference for industrial statisticians, researchers, and practitioners in related fields. The <i>mistat</i> R-package is available from the R CRAN repository.<p/><br></br><p><b> From the Back Cover </b></p></br></br><p><b>The new edition of the prime reference on the tools of statistics used in industry and services, integrating theoretical, practical, and computer-based approaches</b></p><p><i>Modern Industrial Statistics</i> is a leading reference and guide to the statistics tools widely used in industry and services. Designed to help professionals and students easily access relevant theoretical and practical information in a single volume, this standard resource employs a computer-intensive approach to industrial statistics and provides numerous examples and procedures in the popular R language and for MINITAB and JMP statistical analysis software. Divided into two parts, the text covers the principles of statistical thinking and analysis, bootstrapping, predictive analytics, Bayesian inference, time series analysis, acceptance sampling, statistical process control, design and analysis of experiments, simulation and computer experiments, and reliability and survival analysis. Part A, on computer age statistical analysis, can be used in general courses on analytics and statistics. Part B is focused on industrial statistics applications.</p><p>The fully revised third edition covers the latest techniques in R, MINITAB and JMP, and features brand-new coverage of time series analysis, predictive analytics and Bayesian inference. New and expanded simulation activities, examples, and case studies--drawn from the electronics, metal work, pharmaceutical, and financial industries--are complemented by additional computer and modeling methods. Helping readers develop skills for modeling data and designing experiments, this comprehensive volume: </p><ul><li>Explains the use of computer-based methods such as bootstrapping and data visualization</li><li>Covers nonstandard techniques and applications of industrial statistical process control (SPC) charts</li><li>Contains numerous problems, exercises, and data sets representing real-life case studies of statistical work in various business and industry settings</li><li>Includes access to a companion website that contains an introduction to R, sample R code, csv files of all data sets, JMP add-ins, and downloadable appendices</li><li>Provides an author-created R package, <i>mistat</i>, that includes all data sets and statistical analysis applications used in the book</li></ul><p>Part of the acclaimed <i>Statistics in Practice</i> series, <i>Modern Industrial Statistics with Applications in R, MINITAB, and JMP, Third Edition</i>, is the perfect textbook for advanced undergraduate and postgraduate courses in the areas of industrial statistics, quality and reliability engineering, and an important reference for industrial statisticians, researchers, and practitioners in related fields. The <i>mistat</i> R-package is available from the R CRAN repository.</p><p/><br></br><p><b> About the Author </b></p></br></br><p><b>Ron S. Kenett</b> is Chairman of the KPA Group and Senior Research Fellow at the Samuel Neaman Institute, Israel. He is an applied statistician combining expertise in academic, consulting, and business domains. He is a former Professor of Operations Management at The State University of New York at Binghamton, Visiting Scholar at Stanford University, Member of Technical Staff at Bell Laboratories and Director of Statistical Methods for Tadiran Telecom. Ron is a past President of the Israel Statistical Association and of the European Network for Business and Industrial Statistics (ENBIS) and was awarded the 2013 Greenfield Medal by the Royal Statistical Society and the 2018 Box Medal by ENBIS for outstanding contributions to applied statistics. He has authored and co-authored over 250 papers and 14 books.</p><p><b>Shelemyahu Zacks</b> is Distinguished Emeritus Professor of Mathematical Sciences at Binghamton University, Binghamton, New York, USA. He has published 10 books and close to 200 papers. Zacks is known for his groundbreaking articles on change-point problems, common mean problems, Bayes sequential strategies, and reliability analysis. His studies on survival probabilities in crossing minefields and his contributions in stochastic visibility in random fields are regarded as fundamental work in naval research and other defense related areas. He has served on the editorial boards of several prestigious journals including <i>JASA, JSPI</i> and <i>Annals of Statistics</i>, and is a Fellow of many associations including the AMS, ASA and AAAS.</p>

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