Springer Handbook of Engineering Statistics
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 applicati...
|Edition:||1st ed. 2006|
|Collection:||Springer eBooks 2005- - Collection details see MPG.ReNa|
|Summary:||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 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.
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.
|Physical Description:||XLIV, 1120 p online resource|