This handbook gathers the full range of statistical techniques and tools required by engineers, analysts and scientists from all fields. The book is a comprehensive place to look for methods and solutions to practical problems within – but not limited to – data science, quality assurance in design and production engineering.
The tools of engineering statistics are relevant for modeling and prediction of products, processes and services, but also for the analysis of ongoing processes, the reliability and life-cycle assessment of products and services, and finally to achieve realistic predictions on how to improve processes and products.
This book contains contributions from around 115 leading experts in statistics, biostatistics, engineering statistics, reliability engineering, and related areas. It covers the various methods as well as their applications from industrial control to failure mechanism and analysis, medicine, businessintelligence, electronic packaging, and risk management. It enables readers to choose the most appropriate method through its wide range of selection of statistical techniques and tools.
For the second edition all chapters have been thoroughly updated to reflect the current state-of-the-art. Included are also more than 30 completely new contributions revolving around current trends related to modern statistical computing, including: data science, big data, machine learning, optimization, data fusion, high dimensional data, voting systems, life testing, related statistical artificial intelligence (AI) and reliability physics and failure mode mechanisms.
This Springer Handbook of Engineering Statistics provides comprehensive literature with up-to-date statistical methodologies, algorithms, computation methods and tools that can be served as a main reference for researchers, engineers, business analysts, educators and students in all applied fields affected bystatistics.
विषयसूची
Part I: Fundamental Statistics and its Applications.- Part II: Process Monitoring and Improvement.- Part III: Reliability Models and Survival Analysis.- Part IV: Advanced Statistical Methods and Modeling.- Part V: Statistical Computing and Data Mining.- Part VI: Applications in Engineering Statistics.
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Hoang Pham is a distinguished professor and former chairman (2007–2013) of the Department of Industrial and Systems Engineering at Rutgers University. Before joining Rutgers in 1993, he was a senior engineering specialist with the Idaho National Engineering Laboratory, Idaho Falls, Idaho, and Boeing Company in Seattle, Washington. His research areas include reliability modeling and prediction, software reliability, and statistical inference. He is editor-in-chief of the International Journal of Reliability, Quality and Safety Engineering and editor of Springer Series in Reliability Engineering and has been conference chair and program chair of over 40 international conferences and workshops. Dr. Pham is the author or coauthor of 7 books and has published over 200 journal articles, 100 conference papers, and edited 20 books including Springer Handbook of Engineering Statistics and Handbook of Reliability Engineering. He has delivered over 40 invited keynote and plenary speeches at many international conferences and institutions. His numerous awards include the 2009 IEEE Reliability Society Engineer of the Year Award. He is a fellow of the IEEE, AAIA, and IISE.
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