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An introduction to uncertainty in measurement using the GUM (guide to the expression of uncertainty in measurement) / L. Kirkup, R. B. Frenkel

Main Author Kirkup, Les Coauthor Frenkel, R. B. Country Reino Unido. Publication Cambridge : Cambridge University Press, 2006 Description XIII, 233 p. : il. ; 26 cm ISBN 978-0-521-60579-3
0-521-60579-2
CDU 389
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Holdings
Item type Current location Call number Status Date due Barcode Item holds Course reserves
Monografia Biblioteca Geral da Universidade do Minho
BGUM 389 - K Available 430489
Monografia Biblioteca Geral da Universidade do Minho
BGUM 389 - K Available 430490
Monografia Biblioteca Geral da Universidade do Minho
BGUM 389 - K Available 430491

Mestrado Integrado em Engenharia Biológica Laboratórios Integrados de Física 2º semestre

Total holds: 0

Enhanced descriptions from Syndetics:

Measurement shapes scientific theories, characterises improvements in manufacturing processes and promotes efficient commerce. In concert with measurement is uncertainty, and students in science and engineering need to identify and quantify uncertainties in the measurements they make. This book introduces measurement and uncertainty to second and third year students of science and engineering. Its approach relies on the internationally recognised and recommended guidelines for calculating and expressing uncertainty (known by the acronym GUM). The statistics underpinning the methods are considered and worked examples and exercises are spread throughout the text. Detailed case studies based on typical undergraduate experiments are included to reinforce the principles described in the book. This guide is also useful to professionals in industry who are expected to know the contemporary methods in this increasingly important area. Additional online resources are available to support the book at www.cambridge.org/9780521605793.

Table of contents provided by Syndetics

  • Preface (p. xi)
  • 1 The importance of uncertainty in science and technology (p. 1)
  • 1.1 Measurement matters (p. 3)
  • 1.2 Review (p. 13)
  • 2 Measurement fundamentals (p. 15)
  • 2.1 The system of units of measurement (p. 15)
  • 2.2 Scientific and engineering notations (p. 21)
  • 2.3 Rounding and significant figures (p. 22)
  • 2.4 Another way of expressing proportional uncertainty (p. 26)
  • 2.5 Review (p. 26)
  • 3 Terms used in measurement (p. 27)
  • 3.1 Measurement and related terms (p. 27)
  • 3.2 Review (p. 34)
  • 4 Introduction to uncertainty in measurement (p. 35)
  • 4.1 Measurement and error (p. 35)
  • 4.2 Uncertainty is a parameter that characterises the dispersion of values (p. 43)
  • 4.3 Standard deviation as a basic measure of uncertainty (p. 45)
  • 4.4 The uncertainty in the estimate of uncertainty (p. 49)
  • 4.5 Combining standard uncertainties (p. 50)
  • 4.6 Review (p. 52)
  • 5 Some statistical concepts (p. 53)
  • 5.1 Sampling from a population (p. 53)
  • 5.2 The least-squares model and least-squares fitting (p. 59)
  • 5.3 Covariance and correlation (p. 77)
  • 5.4 Review (p. 82)
  • 6 Systematic errors (p. 83)
  • 6.1 Systematic error revealed by specific information (p. 83)
  • 6.2 Systematic error revealed by changed conditions (p. 92)
  • 6.3 Review (p. 96)
  • 7 Calculation of uncertainties (p. 97)
  • 7.1 The measurand model and propagation of uncertainties from inputs to measurand (p. 97)
  • 7.2 Correlated inputs (p. 109)
  • 7.3 Review (p. 125)
  • 8 Probability density, the Gaussian distribution and central limit theorem (p. 126)
  • 8.1 Distribution of scores when tossing coins or dice (p. 126)
  • 8.2 General properties of probability density (p. 128)
  • 8.3 The uniform or rectangular distribution (p. 133)
  • 8.4 The Gaussian distribution (p. 135)
  • 8.5 Experimentally observed non-Gaussian distributions (p. 139)
  • 8.6 The central limit theorem (p. 143)
  • 8.7 Review (p. 153)
  • 9 Sampling a Gaussian distribution (p. 154)
  • 9.1 Sampling the distribution of the mean of a sample of size n, from a Gaussian population (p. 154)
  • 9.2 Sampling the distribution of the variance of a sample of size n, from a Gaussian population (p. 155)
  • 9.3 Sampling the distribution of the standard deviation of a sample of size n, from a Gaussian population (p. 159)
  • 9.4 Review (p. 161)
  • 10 The t-distribution and Welch-Satterthwaite formula (p. 162)
  • 10.1 The coverage interval for a Gaussian distribution (p. 163)
  • 10.2 The coverage interval using a t-distribution (p. 169)
  • 10.3 The Welch-Satterthwaite formula (p. 174)
  • 10.4 Review (p. 185)
  • 11 Case studies in measurement uncertainty (p. 191)
  • 11.1 Reporting measurement results (p. 191)
  • 11.2 Determination of the coefficient of static friction for glass on glass (p. 192)
  • 11.3 A crater-formation experiment (p. 197)
  • 11.4 Determination of the density of steel (p. 203)
  • 11.5 The rate of evaporation of water from an open container (p. 210)
  • 11.6 Review (p. 217)
  • Appendix A Solutions to exercises (p. 218)
  • Appendix B 95% Coverage factors, k, as a function of the number of degrees of freedom, v (p. 222)
  • Appendix C Further discussion following from the Welch-Satterthwaite formula (p. 223)
  • References (p. 226)
  • Index (p. 229)

Reviews provided by Syndetics

CHOICE Review

Because of the importance of measurement, and uncertainty of measurement, in areas of science, medicine, international trade, and others, this important book will enlighten all readers, students as well as professionals, about the need for accuracy, consistency, and clarity when making measurements. Kirkup and Frenkel (Univ. of Technology, Sydney, Australia) use the Guide to the Expression of Uncertainty in Measurement--the GUM, which was published in 1993 by the International Organization for Standardization in Geneva. Used worldwide in collaboration and testing laboratories, medical testing, and the certification of reference materials, these guidelines were developed by several international bodies concerned with measurement and uncertainty in measurement. Many of the guidelines are presented throughout the book. A background in first-year calculus and some basic statistics is necessary for understanding. Some of the topics treated are systematic errors, Gaussian distribution, sampling, and the Welch-Satterthwaite formula. Case studies and exercises are included. ^BSumming Up: Highly recommended. Upper-division undergraduates through professionals. D. J. Gougeon University of Scranton

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