This book provides an historical examination of official science and technology statistics and indicators in Western countries.

This book provides an historical examination of official science and technology statistics and indicators in Western countries.

All measurements are subject to error because no quantity can be known exactly; hence, any measurement has a probability of lying within a certain range. The more precise the measurement, the smaller the range of uncertainty. Uncertainty, Calibration and Probability is a comprehensive treatment of the statistics and methods of estimating these calibration uncertainties. The book features the general theory of uncertainty involving the combination (convolution) of non-Gaussian, student t, and Gaussian distributions; the use of rectangular distributions to represent systematic uncertainties; and measurable and nonmeasurable uncertainties that require estimation. The author also discusses sources of measurement errors and curve fitting with numerous examples of uncertainty case studies. Many useful tables and computational formulae are included as well. All formulations are discussed and demonstrated with the minimum of mathematical knowledge assumed. This second edition offers additional examples in each chapter, and detailed additions and alterations made to the text. New chapters consist of the general theory of uncertainty and applications to industry and a new section discusses the use of orthogonal polynomials in curve fitting. Focusing on practical problems of measurement, Uncertainty, Calibration and Probability is an invaluable reference tool for R&D laboratories in the engineering/manufacturing industries and for undergraduate and graduate students in physics, engineering, and metrology.

This book provide a comprehensive set of modeling methods for data and uncertainty analysis, taking readers beyond mainstream methods and focusing on techniques with a broad range of real-world applications. The book will be useful as a textbook for graduate students, or as a training manual in the fields of calibration and testing. The work may also serve as a reference for metrologists, mathematicians, statisticians, software engineers, chemists, and other practitioners with a general interest in measurement science.

Publisher Description

The internationally recognised methodology for collecting and using R&D statistics, the OECD's Frascati Manual is an essential tool for statisticians and science and innovation policy makers worldwide. It includes definitions of basic concepts, data collection guidelines, and classifications ...

The measurement of scientific, technological, and innovative activities (STI) in the economy is an increasing challenge faced by statistical agencies around the world. Measuring Science, Technology, and Innovation: A Review surveys the current state of the art including the concept of indicators, their quality and use, and a schematic model of the STI system that can identify gaps in the set of indicators commonly in use. The authors review the developments in STI measurement that have taken place in the rest of the world, particularly the widespread use of innovation surveys. The monograph concludes with a discussion of the measurement gaps and issues in the U.S., which we identify as innovation (especially in the service sector), non-R&D investment related to innovation, data timeliness, data linkages, measurement related to public policy goals, and the sources of capital for innovation.After an introduction, Section 2 summarizes the framework for analysis of the STI system that is most widely used by economists. Section 3 considers how the specific data currently collected by the U.S. government, and indicators constructed from those data, relate to the important concepts within the framework. Section 4 expands this discussion by reviewing data collected and indicators published by other countries and international organizations. Section 5 then turns to the issues of policy, and draws explicit connections between important policy questions and indicators, using the framework of Section 2. Section 6 builds on these discussions to highlight gaps and issues with the existing indicators, and Section 7 provides concluding comments.