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AuthorGertsbakh, Ilya. author
TitleMeasurement Theory for Engineers [electronic resource] / by Ilya Gertsbakh
ImprintBerlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003
Connect tohttp://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



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