國立高雄大學圖資館 |

Industrial statisticsa computer-based approach with Python /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Industrial statisticsby Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck.
其他題名:	a computer-based approach with Python /
作者:	Kenett, Ron.
其他作者:	Zacks, Shelemyahu.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xxiii, 472 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Industrial statisticsData processing.
電子資源:	https://doi.org/10.1007/978-3-031-28482-3
ISBN:	9783031284823$q(electronic bk.)

Industrial statisticsa computer-based approach with Python /
Kenett, Ron.

Industrial statisticsa computer-based approach with Python /[electronic resource] :by Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck. - Cham :Springer International Publishing :2023. - xxiii, 472 p. :ill., digital ;24 cm. - Statistics for industry, technology, and engineering,2662-5563. - Statistics for industry, technology, and engineering..

The Role of Statistical Methods in Modern Industry -- Basic Tools and Principles of Process Control -- Advanced Methods of Statistical Process Control -- Multivariate Statistical Process Control -- Classical Design and Analysis of Experiments -- Quality by Design -- Computer Experiments -- Cybermanufacturing and Digital Twins -- Reliability Analysis -- Bayesian Reliability Estimation and Prediction -- Sampling Plans for Batch and Sequential Inspection.

This innovative textbook presents material for a course on industrial statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others. The first chapters of the text focus on the basic tools and principles of process control, methods of statistical process control (SPC), and multivariate SPC. Next, the authors explore the design and analysis of experiments, quality control and the Quality by Design approach, computer experiments, and cybermanufacturing and digital twins. The text then goes on to cover reliability analysis, accelerated life testing, and Bayesian reliability estimation and prediction. A final chapter considers sampling techniques and measures of inspection effectiveness. Each chapter includes exercises, data sets, and applications to supplement learning. Industrial Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. In addition, it can be used in focused workshops combining theory, applications, and Python implementations. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Modern Statistics: A Computer-Based Approach with Python. It covers topics such as probability models and distribution functions, statistical inference and bootstrapping, time series analysis and predictions, and supervised and unsupervised learning. These texts can be used independently or for consecutive courses. This book is part of an impressive and extensive write up enterprise (roughly 1,000 pages!) which led to two books published by Birkhäuser. This book is on Industrial Statistics, an area in which the authors are recognized as major experts. The book combines classical methods (never to be forgotten!) and "hot topics" like cyber manufacturing, digital twins, A/B testing and Bayesian reliability. It is written in a very accessible style, focusing not only on HOW the methods are used, but also on WHY. In particular, the use of Python, throughout the book is highly appreciated. Python is probably the most important programming language used in modern analytics. The authors are warmly thanked for providing such a state-of-the-art book. It provides a comprehensive illustration of methods and examples based on the authors longstanding experience, and accessible code for learning and reusing in classrooms and on-site applications. Professor Fabrizio Ruggeri Research Director at the National Research Council, Italy President of the International Society for Business and Industrial Statistics (ISBIS) Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)

ISBN: 9783031284823$q(electronic bk.)

Standard No.: 10.1007/978-3-031-28482-3doiSubjects--Topical Terms:

956332
Industrial statistics
--Data processing.

LC Class. No.: HB137

Dewey Class. No.: 338.0021

Industrial statisticsa computer-based approach with Python /
LDR:04811nmm a2200361 a 4500 001 647111
003 DE-He213
005 20230616220123.0
006 m d
007 cr nn 008maaau
008 240116s2023 sz s 0 eng d
020 $a 9783031284823$q(electronic bk.)
020 $a 9783031284816$q(paper)
024 7 $a 10.1007/978-3-031-28482-3 $2 doi
035 $a 978-3-031-28482-3
040 $a GP $c GP
041 0 $a eng
050 4 $a HB137
072 7 $a PBT $2 bicssc
072 7 $a UFM $2 bicssc
072 7 $a COM077000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a UFM $2 thema
082 0 4 $a 338.0021 $2 23
090 $a HB137 $b .K33 2023
100 1 $a Kenett, Ron. $3 507058
245 1 0 $a Industrial statistics $h [electronic resource] : $b a computer-based approach with Python / $c by Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck.
260 $a Cham : $b Springer International Publishing : $b Imprint: Birkhäuser, $c 2023.
300 $a xxiii, 472 p. : $b ill., digital ; $c 24 cm.
490 1 $a Statistics for industry, technology, and engineering, $x 2662-5563
505 0 $a The Role of Statistical Methods in Modern Industry -- Basic Tools and Principles of Process Control -- Advanced Methods of Statistical Process Control -- Multivariate Statistical Process Control -- Classical Design and Analysis of Experiments -- Quality by Design -- Computer Experiments -- Cybermanufacturing and Digital Twins -- Reliability Analysis -- Bayesian Reliability Estimation and Prediction -- Sampling Plans for Batch and Sequential Inspection.
520 $a This innovative textbook presents material for a course on industrial statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others. The first chapters of the text focus on the basic tools and principles of process control, methods of statistical process control (SPC), and multivariate SPC. Next, the authors explore the design and analysis of experiments, quality control and the Quality by Design approach, computer experiments, and cybermanufacturing and digital twins. The text then goes on to cover reliability analysis, accelerated life testing, and Bayesian reliability estimation and prediction. A final chapter considers sampling techniques and measures of inspection effectiveness. Each chapter includes exercises, data sets, and applications to supplement learning. Industrial Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. In addition, it can be used in focused workshops combining theory, applications, and Python implementations. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Modern Statistics: A Computer-Based Approach with Python. It covers topics such as probability models and distribution functions, statistical inference and bootstrapping, time series analysis and predictions, and supervised and unsupervised learning. These texts can be used independently or for consecutive courses. This book is part of an impressive and extensive write up enterprise (roughly 1,000 pages!) which led to two books published by Birkhäuser. This book is on Industrial Statistics, an area in which the authors are recognized as major experts. The book combines classical methods (never to be forgotten!) and "hot topics" like cyber manufacturing, digital twins, A/B testing and Bayesian reliability. It is written in a very accessible style, focusing not only on HOW the methods are used, but also on WHY. In particular, the use of Python, throughout the book is highly appreciated. Python is probably the most important programming language used in modern analytics. The authors are warmly thanked for providing such a state-of-the-art book. It provides a comprehensive illustration of methods and examples based on the authors longstanding experience, and accessible code for learning and reusing in classrooms and on-site applications. Professor Fabrizio Ruggeri Research Director at the National Research Council, Italy President of the International Society for Business and Industrial Statistics (ISBIS) Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)
650 0 $a Industrial statistics $x Data processing. $3 956332
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Statistics and Computing. $3 922173
650 2 4 $a Applied Statistics. $3 805583
700 1 $a Zacks, Shelemyahu. $3 795023
700 1 $a Gedeck, Peter. $3 936007
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Statistics for industry, technology, and engineering. $3 853878
856 4 0 $u https://doi.org/10.1007/978-3-031-28482-3
950 $a Mathematics and Statistics (SpringerNature-11649)