This book provide a comprehensive set of modeling methods for data and uncertainty analysis, taking readers beyond mainstream methods described in standard texts. The focus is on techniques having a broad range of real-world applications in a variety of fields.

Data Modeling for Metrology and Testing in Measurement Science may be used as a textbook in graduate courses on data modeling and computational methods, or as a training manual in the fields of calibration and testing. The book will also serve as an excellent reference for metrologists, mathematicians, statisticians, software engineers, chemists, and other practitioners with a general interest in measurement science.

Klappentext

This book and companion DVD provide a comprehensive set of modeling methods for data and uncertainty analysis, taking readers beyond mainstream methods described in standard texts. The emphasis throughout is on techniques having a broad range of real-world applications in measurement science.
Mainstream methods of data modeling and analysis typically rely on certain assumptions that do not hold for many practical applications. Developed in this work are methods and computational tools to address general models that arise in practice, allowing for a more valid treatment of calibration and test data and providing a deeper understanding of complex situations in measurement science.
Additional features and topics of the book include:

• Introduction to modeling principles in metrology and testing
• Presentation of a basic probability framework in metrology and statistical approaches to uncertainty assessment
• Discussion of the latest developments in data analysis using least squares, Fast Fourier Transform, wavelets, and fuzzy logic methods
• Data fusion using neural networks, fuzzy methods, decision making, and risk analysis
• A computer-assisted, rigorous approach to data evaluation and analysis of measurement software validity
• Introduction to virtual instruments, and an overview of IT tools for measurement science
  Data Modeling for Metrology and Testing in Measurement Science may be used as a textbook in graduate courses on data modeling and computational methods, or as a training manual in the fields of calibration and testing. The book will also serve as an excellent reference for metrologists, mathematicians, statisticians, software engineers, chemists, and other practitioners with a general interest in measurement science.

Zusammenfassung
The aim of this book is to provide, ?rstly, an introduction to probability and statistics especially directed to the metrology and testing ?elds and secondly, a comprehensive, newer set of modelling methods for data and uncertainty analysis that are generally not considered yet within mainstream methods. The book brings, for the ?rst time, a coherent account of these newer me- ods and their computational implementation. They are potentially important because they address problems in application ?elds where the usual hypot- ses that are at the basis of most of the traditional statistical and probabilistic methods, for example, relating to normality of the probability distributions, are frequently not ful?lled to such an extent that an accurate treatment of the calibration or test data using standard approaches is not possible. Additi- ally, the methods can represent alternative ways of data analysis, allowing a deeper understanding of complex situations in measurement. The book lends itself as a possible textbook for undergraduate or postgraduate study in an area where existing texts focus mainly on the most common and well-known methods that do not encompass modern approaches to calibration and testing problems. The book is structured in such a way to guide readers with only a g- eral interest in measurement issues through a series of review papers, from an initial introduction to modelling principles in metrology and testing, to the basic principles of probability in metrology and statistical approaches to - certainty assessment.

Inhalt
An Introduction to Data Modeling Principles in Metrology and Testing.- Probability in Metrology.- Three Statistical Paradigms for the Assessment and Interpretation of Measurement Uncertainty.- Interval Computations and Interval-Related Statistical Techniques: Tools for Estimating Uncertainty of the Results of Data Processing and Indirect Measurements.- Parameter Estimation Based on Least Squares Methods.- Frequency and Time#x2014;Frequency Domain Analysis Tools in Measurement.- Data Fusion, Decision-Making, and Risk Analysis: Mathematical Tools and Techniques.- Comparing Results of Chemical Measurements: Some Basic Questions from Practice.- Modelling of Measurements, System Theory and Uncertainty Evaluation.- Approaches to Data Assessment and Uncertainty Estimation in Testing.- Monte Carlo Modeling of Randomness.- Software Validation and Preventive Software Quality Assurance for Metrology.- Virtual Istrumentation.- Internet-Enabled Metrology.