Author | Gertsbakh, Ilya. author |
---|---|

Title | Measurement Theory for Engineers [electronic resource] / by Ilya Gertsbakh |

Imprint | Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003 |

Connect to | http://dx.doi.org/10.1007/978-3-662-08583-7 |

Descript | XIII, 150 p. online resource |

SUMMARY

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

CONTENT

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 Fโ{128}{148}distribution -- References

Mathematics
Probabilities
Physical measurements
Measurement
Statistics
Applied mathematics
Engineering mathematics
Industrial engineering
Production engineering
Quality control
Reliability
Industrial safety
Mathematics
Probability Theory and Stochastic Processes
Measurement Science and Instrumentation
Appl.Mathematics/Computational Methods of Engineering
Quality Control Reliability Safety and Risk
Statistics for Engineering Physics Computer Science Chemistry and Earth Sciences
Industrial and Production Engineering