Advertisement

Table of contents

  1. Front Matter
    Pages i-xliv
  2. Fundamental Statistics and Its Applications

    1. Front Matter
      Pages 1-169
    2. Hoang Pham
      Pages 3-48
    3. Paul Kvam, Jye-Chyi Lu
      Pages 49-61
    4. Chin-Diew Lai, D.N. Murthy, Min Xie
      Pages 63-78
    5. D.N. Murthy, Jaiwook Baik, Richard Wilson, Michael Bulmer
      Pages 97-111
    6. Karl Sigman
      Pages 137-152
  3. Process Monitoring and Improvement

    1. Front Matter
      Pages 171-344
    2. Wei Jiang, Terrence Murphy, Kwok-Leung Tsui
      Pages 173-192
    3. Kailash Kapur, Qianmei Feng
      Pages 193-212
    4. Susan Albin, Di Xu
      Pages 213-227
    5. Kai-Tai Fang, Ling-Yau Chan
      Pages 229-247
    6. Raj Govindaraju
      Pages 263-279
    7. Philippe Castagliola, Giovanni Celano, Sergio Fichera
      Pages 291-325
  4. Reliability Models and Survival Analysis

    1. Front Matter
      Pages 345-520
    2. Chengjie Xiong, Kejun Zhu, Kai Yu
      Pages 347-367
    3. Maxim Finkelstein, Veronica Esaulova
      Pages 369-386
    4. Wei Wang, Chengcheng Hu
      Pages 387-396
    5. Francis Pascual, William Meeker, Luis Escobar
      Pages 397-426
    6. Raymond Paul, Wei-Tek Tsai, Yinong Chen, Chun Fan, Zhibin Cao, Hai Huang
      Pages 443-476
  5. Regression Methods and Data Mining

    1. Front Matter
      Pages 521-669
    2. Wei-Yin Loh
      Pages 537-549
    3. Nan Lin, Douglas Noe, Xuming He
      Pages 551-570
    4. Fenghai Duan, Heping Zhang
      Pages 607-621
    5. Weichuan Yu, Baolin Wu, Tao Huang, Xiaoye Li, Kenneth Williams, Hongyu Zhao
      Pages 623-638
    6. Miyoung Shin, Amrit Goel
      Pages 639-649
    7. Kwok-Leung Tsui, Victoria Chen, Wei Jiang, Y. Aslandogan
      Pages 651-669
  6. Modeling and Simulation Methods

    1. Front Matter
      Pages 671-848
    2. Yi Li
      Pages 687-703
    3. Mirjam Moerbeek
      Pages 705-718
    4. Joseph Naus
      Pages 775-790
    5. Shang-Kuo Yang
      Pages 791-805

About this book

Introduction

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.

Keywords

Applied statistics and sciene Biostatistics Engineering statistics Environmental statistics MRWCat2006 Quality control dgao 2007

Editors and affiliations

  • Hoang Pham
    • 1
  1. 1.Department of Industrial and Systems EngineeringRutgers the State University of New JerseyPiscatawayUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84628-288-1
  • Copyright Information Springer-Verlag London 2006
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-85233-806-0
  • Online ISBN 978-1-84628-288-1
  • Buy this book on publisher's site
Industry Sectors
Pharma
Materials & Steel
Automotive
Chemical Manufacturing
Biotechnology
Electronics
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering