Introduction to Statistics in Metrology - by Stephen Crowder & Collin Delker & Eric Forrest & Nevin Martin (Paperback)

Book Synopsis

This book provides an overview of the application of statistical methods to problems in metrology, with emphasis on modelling measurement processes and quantifying their associated uncertainties. It covers everything from fundamentals to more advanced special topics, each illustrated with case studies from the authors' work in the Nuclear Security Enterprise (NSE). The material provides readers with a solid understanding of how to apply the techniques to metrology studies in a wide variety of contexts.

The volume offers particular attention to uncertainty in decision making, design of experiments (DOEx) and curve fitting, along with special topics such as statistical process control (SPC), assessment of binary measurement systems, and new results on sample size selection in metrology studies. The methodologies presented are supported with R script when appropriate, and the code has been made available for readers to use in their own applications. Designed to promote collaboration between statistics and metrology, this book will be of use to practitioners of metrology as well as students and researchers in statistics and engineering disciplines.

From the Back Cover

This book provides an overview of the application of statistical methods to problems in metrology, with emphasis on modelling measurement processes and quantifying their associated uncertainties. It covers everything from fundamentals to more advanced special topics, each illustrated with case studies from the authors' work in the Nuclear Security Enterprise (NSE). The material provides readers with a solid understanding of how to apply the techniques to metrology studies in a wide variety of contexts.

The volume offers particular attention to uncertainty in decision making, design of experiments (DOEx) and curve fitting, along with special topics such as statistical process control (SPC), assessment of binary measurement systems, and new results on sample size selection in metrology studies. The methodologies presented are supported with R script when appropriate, and the code has been made available for readers to use in their own applications. Designed to promote collaboration between statistics and metrology, this book will be of use to practitioners of metrology as well as students and researchers in statistics and engineering disciplines.

About the Author

Stephen Crowder is a Principal Member of Technical Staff in the Statistical Sciences Department at Sandia National Laboratories with over thirty years of experience working in industrial statistics and metrology. He received his B.S. degree in Mathematics from Abilene Christian University and his M.S. and Ph.D. in Statistics from Iowa State University. Stephen has previously done research and published in the fields of statistical process control, reliability, and statistics in metrology.

Collin Delker is a Senior Member of Technical Staff in the Primary Standards Laboratory at Sandia National Laboratories. He received his B.S. degree in Electrical Engineering from Kansas State University and his Ph.D. in Electrical Engineering, with an emphasis in microelectronics and nanotechnology, from Purdue University. Collin specializes in developing techniques for the calibration of microwave frequency devices in addition to providing software solutions for uncertainty analysis.

Eric Forrest is a Principal Member of Technical Staff in the Primary Standards Laboratory at Sandia National Laboratories where he leads the Radiation & Optics Project. He received his B.S., M.S., and Ph.D. in Nuclear Science & Engineering from MIT, where he was a National Nuclear Security Administration Fellow. His research focused on high speed optical/infrared imaging and development of nanoengineered surfaces for enhanced heat transfer in nuclear thermal hydraulics applications. Eric specializes in uncertainty analyses for complex experimental measurements.

Nevin Martin is a Member of the Technical Staff in the Statistical Sciences Department at Sandia National Laboratories. She received her B.S. degree in Finance from the University of Arizona and her M.S. degree in Statistics from the University of New Mexico. Nevin collaborates on a wide range of projects that include work in statistical computing with R, data visualization and modeling, and uncertainty quantification. She teaches a short course on "Introduction to Statistical Computing in R" and develops R-code for the application of statistics in metrology.