Synopses & Reviews
Engineers and practitioners contribute to society through their ability to apply basic scientific principles to real problems in an effective and efficient manner. They must collect data to test their products every day as part of the design and testing process and also after the product or process has been rolled out to monitor its effectiveness. Model building and validation, data collection, data analysis and data interpretation form the core of sound engineering practice. After the data has been gathered the engineers, statisticians, designers, and practitioners must be able to sift them and interpret them correctly so that meaning can be exposed from a mass of undifferentiated numbers or facts. To do this he must be familiar with the fundamental concepts of correlation, uncertainty, variability and risk in the face of uncertainty. In today's global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. Many organizations have shown that the first step to continuous improvement is to integrate the widespread use of statistics and basic data analysis into the manufacturing development process as well as into the day-to-day business decisions taken in regard to engineering and technological information processes. The Springer Handbook of Engineering Statistics gathers together the full range of statistical techniques required by readers from all fields to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved. Key Topics Fundamental Statistics Process Monitoring and Improvement Reliability Modeling and Survival Analysis Regression Methods Data Mining Statistical Methods and Modeling Wide Range of Applications including Six Sigma Features Contributions from leading experts in statistics and their application to engineering from industrial control to academic medicine and financial risk management Wide-ranging selection of statistical techniques to enable the readers to choose the method most appropriate Extensive and easy-to-use subject index making information quickly available to the reader. The Springer Handbook of Engineering Statistics will be essential reading for all engineers, statisticians, researchers, teachers, students, and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.
In today's global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. Many organisations have shown that the first step to continuous improvement is to integrate the widespread use of statistics and basic data analysis into the manufacturing development process as well as into the day-to-day business decisions taken in regard to engineering processes.
The "Springer Handbook of Engineering Statistics" gathers together the full range of statistical techniques required by engineers from all fields to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved.
In today's global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. This book gathers together the full range of statistical techniques required by engineers from all fields. It will assist them to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved. The handbook will be essential reading for all engineers and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.
About the Author
Dr. Hoang Pham Pham is Professor and Director of the Undergraduate Program in the Department of Industrial and Systems Engineering at Rutgers University, Piscataway, NJ. Before joining Rutgers, he was a senior engineering specialist at the Boeing Company, Seattle, and the Idaho National Engineering Laboratory, Idaho Falls. His research interests include software reliability, system reliability modeling, maintenance, and environmental risk assessment. He is the author of Software Reliability (Springer-Verlag, 2000) and a forthcoming book System Software Reliability (Springer, 2005). He is the editor of the Handbook of Reliability Engineering (Springer-Verlag, 2003) and Springer Handbook of Engineering Statistics (Springer, 2005). He is also the editor of Springer Series in Reliability. He has published more than 80 journal articles, 20 book chapters, and the editor of ten volumes. He is editor-in-chief of the International Journal of Reliability, Quality and Safety Engineering (www.worldscinet.com/ijrqse), associate editor of the IEEE Trans. on Systems, Man and Cybernetics (Part A), and guest editor of IIE Transactions and IEEE Trans. on Systems, Man and Cybernetics (Part A). He has been conference chair and program chair of over 20 international conferences and workshops and is currently the Conference Chair of the Eleventh International Conference on Reliability and Quality in Design will be held in St. Louis , August 2005. He received the B.S. degree in mathematics, B.S. degree in computer science, both with high honors, from Northeastern Illinois University , Chicago , the M.S. degree in statistics from the University of Illinois , Urbana-Champaign, and the M.S. and Ph.D. degrees in industrial engineering from the State University of New York at Buffalo .
Table of Contents
Part A Fundamental Statistics and Its Applications 1. Statistical Reliability with Applications (Paul Kvam and Jye-Chyi Lu ).- 2. Weibull Distributions and Their Applications (C. D. Lai, D.N.P. Murthy, Min Xie).- 3. Characterizations of Probability Distributions (H. N. Nagaraja) 4. Two-Dimensional Failure Modelling (D.N.P. Murthy, J. Baik, Richard J. Wilson, M. R. Bulmer).- 5. Prediction Intervals for Reliability Growth Models with Small Sample Sizes (John Quigley, Lesley Walls ).- 6. Promotional Warranty Policies: Analysis and Perspectives (Jun Bai, Hoang Pham).- 7. Stationary Marked Point Processes (Karl Sigman).- 8. Modeling and Analyzing Yield, Burn-in and Reliability for Semiconductor Manufacturing: Overview (Way Kuo, Kyungmee O. Kim , Taeho Kim).- Part B Process Monitoring and Improvement 9. Statistical Methods for Quality and Productivity Improvement (Wei Jiang, Terrence E. Murphy, Kwok-Leung Tsui).- 10. Statistical Methods for Product and Process Improvement (Kailash C. Kapur, Qianmei Feng).- 11. Robust Optimization in Quality Engineering (Di Xu, Susan L. Albin).- 12. Uniform Design and Its Industrial Applications (Kai-Tai Fang , Ling-Yau Chan).- 13. Cuscore Statistics: Directed Process Monitoring for Early Problem Detection (Harriet Black Nembhard).- 14. Chain Sampling (Raj K. Govindaraju).- 15. Some Statistical Models for the Monitoring of High-Quality Processes (Min Xie, T N. Goh).- 16. Multivariate Statistical Process Control Schemes for Controlling a Mean (Richard A.Johnson, Ruojia Li).- Part C Reliability Models and Survival Analysis 17. Statistical Survival Analysis With Applications (Chengjie Xiong, Kejun Zhu, Kai Yu).- 18. Proportional Hazards Regression Models (Wei Wang, Chengcheng Hu).- 19. Accelerated Life Test Models and Data Analysis (Francis G. Pascual, William Q. Meeker, , Luis A. Escobar).- 20. Statistical Approaches to Planning of Accelerated Reliability Testing (Loon-Ching Tang).- 21. E2E Testing, Evaluation of High Assurance Systems (Ray Paul, Wei-Tek Tsai, Yinong Chen, Chun Fan, Zhibin Cao, Xinxin Liu, Hai Huang).- 22. Statistical Models in Software Reliability and Operations Research (P. K. Kapur, A.K. Bardhan).- 23. An Experimental Study of Human Factors in Software Reliability based on a Quality Engineering Approach (Zhigeru Yamada ).- 24. Statistical Models for Predicting Reliability of Software Systems in Random Environments (Hoang Pham , Xiaolin Teng).- Part D Regression Methods and Data Mining 25. Logistic Regression Tree Analysis (Wei-Yin Loh).- 26. Tree-Based Methods and Their Applications (Nan Lin, Douglas Noe, Xuming He).- 27. Image Registration and Unknown Coordinate Systems (Theodore Chang).- 28. Statistical Genetics for Genomic Data Analysis (Jae K. Lee).- 29. Statistical Methodologies for Analyzing Genomic Data (Fenghai Duan , Heping Zhang).- 30. Statistical Methods in Proteomics (Weichuan Yu, Baolin Wu, Tao Huang, Xiaoye Li, Kenneth Williams, Hongyu Zhao).- 31. Radial Basis Functions for Data Mining (Miyoung Shin, Amrit L. Goel).- 32. Data Mining Methods and Applications (Kwok-Leung Tsui , Victoria C. P. Chen, Wei Jiang, Y. Alp Aslandogan).- 33. Support Vector Machines for Data Modeling With Software Engineering Applications (Hojung Lim, Amrit L. Goel).- Part E Statistical Methods, Modeling and Applications 34. Bootstrap, Markov Chain and Estimating Function (Feifang Hu).- 35. Random Effects Models (Yi Li).- 36. A Two-Way Semi-Linear Model for Normalization and Analysis of Microarray Data (Jian Huang, Cun-Hui Zhang).- 37. Latent Variable Models for Longitudinal Data With Flexible Measurement Schedule (Haiqun Lin).- 38. Genetic Algorithms and Its Applications (Mitsuo Gen).- 39. Scan Statistics (Joseph I. Naus).- 40. Condition-Based Failure Prediction (S. K. Yang).- 41. Statistical Maintenance Modeling for Complex Systems (Wenjian Li, Hoang Pham).- 42. Stochastic Models on Maintenance (Toshio Nakagawa) About the Authors Index