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Data Modeling for Metrology and Testing in Measurement Science

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Cover of 'Data Modeling for Metrology and Testing in Measurement Science'

Table of Contents

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    Book Overview
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    Chapter 1 An Introduction to Data Modeling Principles in Metrology and Testing
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    Chapter 2 Probability in Metrology
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    Chapter 3 Three Statistical Paradigms for the Assessment and Interpretation of Measurement Uncertainty
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    Chapter 4 Interval Computations and Interval-Related Statistical Techniques: Tools for Estimating Uncertainty of the Results of Data Processing and Indirect Measurements
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    Chapter 5 Parameter Estimation Based on Least Squares Methods
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    Chapter 6 Frequency and Time—Frequency Domain Analysis Tools in Measurement
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    Chapter 7 Data Fusion, Decision-Making, and Risk Analysis: Mathematical Tools and Techniques
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    Chapter 8 Comparing Results of Chemical Measurements: Some Basic Questions from Practice
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    Chapter 9 Modelling of Measurements, System Theory and Uncertainty Evaluation
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    Chapter 10 Approaches to Data Assessment and Uncertainty Estimation in Testing
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    Chapter 11 Monte Carlo Modeling of Randomness
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    Chapter 12 Software Validation and Preventive Software Quality Assurance for Metrology
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    Chapter 13 Virtual Istrumentation
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    Chapter 14 Internet-Enabled Metrology
Attention for Chapter 11: Monte Carlo Modeling of Randomness
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Chapter title
Monte Carlo Modeling of Randomness
Chapter number 11
Book title
Data Modeling for Metrology and Testing in Measurement Science
Published by
Birkhäuser Boston, January 2009
DOI 10.1007/978-0-8176-4804-6_11
Book ISBNs
978-0-8176-4592-2, 978-0-8176-4804-6
Authors

Alan G. Steele, Robert J. Douglas, Steele, Alan G., Douglas, Robert J.

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