Overview
- Takes the reader beyond mainstream methods described in standard texts on data and uncertainty analysis
- Real-world applications in a variety of fields, including chemistry, software engineering, and metrology
- For a broad audience of graduate students, researchers, and practitioners in metrology, mathematics, statistics, chemistry, and software engineering
- May be used as a textbook in graduate courses on modeling and computational methods, or as a training manual in the fields of calibration and testing
- Includes supplementary material: sn.pub/extras
Part of the book series: Modeling and Simulation in Science, Engineering and Technology (MSSET)
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Table of contents (14 chapters)
Keywords
About this book
Reviews
From the reviews:
“This is a surprisingly eclectic compilation that I found full of interesting concepts and new knowledge. … Academic researchers and National Measurement Institutes will certainly recommend the text to their libraries. Their research students will benefit … .” (D. Brynn Hibbert, Accreditation and Quality Assurance, Vol. 15, 2010)Editors and Affiliations
Bibliographic Information
Book Title: Data Modeling for Metrology and Testing in Measurement Science
Editors: Franco Pavese, Alistair B. Forbes
Series Title: Modeling and Simulation in Science, Engineering and Technology
DOI: https://doi.org/10.1007/978-0-8176-4804-6
Publisher: Birkhäuser Boston, MA
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Birkhäuser Boston 2009
Hardcover ISBN: 978-0-8176-4592-2Published: 17 December 2008
eBook ISBN: 978-0-8176-4804-6Published: 16 December 2008
Series ISSN: 2164-3679
Series E-ISSN: 2164-3725
Edition Number: 1
Number of Pages: XVIII, 486
Number of Illustrations: 111 b/w illustrations
Topics: Mathematical Modeling and Industrial Mathematics, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Industrial and Production Engineering, Computational Mathematics and Numerical Analysis, Statistics and Computing/Statistics Programs, Probability Theory and Stochastic Processes