Brief but plain introduction into measurement theory
Based on lectures for students in industrial engineering
Most of the chapters are followd by exercises
Well written textbook on industrial applications of Statistical Measurement Theory. It deals with the principal issues of measurement theory, is concise and intelligibly written, and to a wide extent self-contained. Difficult theoretical issues are separated from the mainstream presentation. Each topic starts with an informal introduction followed by an example, the rigorous problem formulation, solution method, and a detailed numerical solution. Chapter are concluded with a set of exercises of increasing difficulty, mostly with solutions. Knowledge of calculus and fundamental probability and statistics is assumed.
|The material in this book was first presented as a one-semester graduate course in Measurement Theory for M.Sc. students of the Industrial Engineering De partment of Ben Gurion University in the 2000/2001 academic year. The book is devoted to various aspects of the statistical analysis of data arising in the process of measurement. We would like to stress that the book is devoted to general problems arising in processing measurement data and does not deal with various aspects of special measurement techniques. For example, we do not go into the details of how special physical parameters, say ohmic resistance or temperature, should be measured. We also omit the accuracy analysis of particular measurement devices. The Introduction (Chapter 1) gives a general and brief deillegalscription of the measurement process, defines the measurand and describes different kinds of the measurement error. Chapter 2 is devoted to the point and interval estimation of the popula tion mean and standard deviation (variance). It also discusses the normal and uniform distributions, the two most widely used distributions in measurement. We give an overview of the basic rules for operating with means and variances of sums of random variables. This information is particularly important for combining measurement results obtained from different sources. There is a brief deillegalscription of graphical tools for analyzing sampIe data. This chapter also presents the round-off rules for data presentation.
1 Introduction.- 2 Mean and Standard Deviation.- 3 Comparing Means and Variances.- 4 Sources of Uncertainty: Process and Measurement Variability.- 5 Measurement Uncertainty: Error Propagation Formula.- 6 Calibration of Measurement Instruments.- 7 Collaborative Studies.- 8 Measurements in Special Circumstances.- Answers and Solutions to Exercises.- Appendix A: Normal Distribution.- Appendix B: Quantiles of the Chi-Square Distribution.- Appendix C: Critical Values of the F—distribution.- References.
The emphasis of this textbook is on industrial applications of Statistical Measurement Theory. It deals with the principal issues of measurement theory, is concise and intelligibly written, and to a wide extent self-contained. Difficult theoretical issues are separated from the mainstream presentation. Each topic starts with an informal introduction followed by an example, the rigorous problem formulation, solution method, and a detailed numerical solution. Each chapter concludes with a set of exercises of increasing difficulty, mostly with solutions. The book is meant as a text for graduate students and a reference for researchers and industrial experts specializing in measurement and measurement data analysis for quality control, quality engineering and industrial process improvement using statistical methods. Knowledge of calculus and fundamental probability and statistics is required for the understanding of its contents.
From the reviews:
"This textbook deals with the principal issues of measurement theory. Each topic starts with an informal introduction followed by an example, the rigorous problem formulation, solution method, and a detailed numerical solution. ... This book is intended as a text for graduate students and a reference for researchers and industrial experts specializing in measurement data analysis for quality control and industrial process improvement." (Ivan Krivý, Zentralblatt MATH, Vol. 1018, 2003)
The material in this book was first presented as a one-semester graduate course in Measurement Theory for M.Sc. students of the Industrial Engineering De partment of Ben Gurion University in the 2000/2001 academic year. The book is devoted to various aspects of the statistical analysis of data arising in the process of measurement. We would like to stress that the book is devoted to general problems arising in processing measurement data and does not deal with various aspects of special measurement techniques. For example, we do not go into the details of how special physical parameters, say ohmic resistance or temperature, should be measured. We also omit the accuracy analysis of particular measurement devices. The Introduction (Chapter 1) gives a general and brief deillegalscription of the measurement process, defines the measurand and describes different kinds of the measurement error. Chapter 2 is devoted to the point and interval estimation of the popula tion mean and standard deviation (variance). It also discusses the normal and uniform distributions, the two most widely used distributions in measurement. We give an overview of the basic rules for operating with means and variances of sums of random variables. This information is particularly important for combining measurement results obtained from different sources. There is a brief deillegalscription of graphical tools for analyzing sampIe data. This chapter also presents the round-off rules for data presentation.
1 Introduction.- 2 Mean and Standard Deviation.- 3 Comparing Means and Variances.- 4 Sources of Uncertainty: Process and Measurement Variability.- 5 Measurement Uncertainty: Error Propagation Formula.- 6 Calibration of Measurement Instruments.- 7 Collaborative Studies.- 8 Measurements in Special Circumstances.- Answers and Solutions to Exercises.- Appendix A: Normal Distribution.- Appendix B: Quantiles of the Chi-Square Distribution.- Appendix C: Critical Values of the F-distribution.- References.
From the reviews:
"This textbook deals with the principal issues of measurement theory. Each topic starts with an informal introduction followed by an example, the rigorous problem formulation, solution method, and a detailed numerical solution. ... This book is intended as a text for graduate students and a reference for researchers and industrial experts specializing in measurement data analysis for quality control and industrial process improvement." (Ivan Krivý, Zentralblatt MATH, Vol. 1018, 2003)
Inhaltsverzeichnis
1 Introduction.- 2 Mean and Standard Deviation.- 3 Comparing Means and Variances.- 4 Sources of Uncertainty: Process and Measurement Variability.- 5 Measurement Uncertainty: Error Propagation Formula.- 6 Calibration of Measurement Instruments.- 7 Collaborative Studies.- 8 Measurements in Special Circumstances.- Answers and Solutions to Exercises.- Appendix A: Normal Distribution.- Appendix B: Quantiles of the Chi-Square Distribution.- Appendix C: Critical Values of the F¿distribution.- References.
Klappentext
The material in this book was first presented as a one-semester graduate course in Measurement Theory for M.Sc. students of the Industrial Engineering De partment of Ben Gurion University in the 2000/2001 academic year. The book is devoted to various aspects of the statistical analysis of data arising in the process of measurement. We would like to stress that the book is devoted to general problems arising in processing measurement data and does not deal with various aspects of special measurement techniques. For example, we do not go into the details of how special physical parameters, say ohmic resistance or temperature, should be measured. We also omit the accuracy analysis of particular measurement devices. The Introduction (Chapter 1) gives a general and brief deillegalscription of the measurement process, defines the measurand and describes different kinds of the measurement error. Chapter 2 is devoted to the point and interval estimation of the popula tion mean and standard deviation (variance). It also discusses the normal and uniform distributions, the two most widely used distributions in measurement. We give an overview of the basic rules for operating with means and variances of sums of random variables. This information is particularly important for combining measurement results obtained from different sources. There is a brief deillegalscription of graphical tools for analyzing sampIe data. This chapter also presents the round-off rules for data presentation.
Brief but plain introduction into measurement theoryBased on lectures for students in industrial engineeringMost of the chapters are followd by exercises
