A student's guide to data and error analysis [electronic resource] / Herman J.C. Berendsen.
Berendsen, Herman J. C.
2011
QA275 .B43 2011
Available Online

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Title
A student's guide to data and error analysis [electronic resource] / Herman J.C. Berendsen.
Author
Berendsen, Herman J. C.
ISBN
9780521119405
9780521134927
9781139079822 (electronic book)
Publication Details
Cambridge ; New York : Cambridge University Press, 2011.
Language
English
Description
xii, 225 p. : ill.
Call Number
QA275 .B43 2011
Dewey Decimal Classification
511/.43
Summary
"All students taking laboratory courses within the physical sciences and engineering will benefit from this book, whilst researchers will find it an invaluable reference. This concise, practical guide brings the reader up-to-speed on the proper handling and presentation of scientific data and its inaccuracies. It covers all the vital topics with practical guidelines, computer programs (in Python), and recipes for handling experimental errors and reporting experimental data. In addition to the essentials, it also provides further background material for advanced readers who want to understand how the methods work. Plenty of examples, exercises and solutions are provided to aid and test understanding, whilst useful data, tables and formulas are compiled in a handy section for easy reference"-- Provided by publisher.
Note
Includes index.
Access Note
Access limited to authorized users.
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Machine generated contents note: Part I. Data and Error Analysis: 1. Introduction; 2. The presentation of physical quantities with their inaccuracies; 3. Errors: classification and propagation; 4. Probability distributions; 5. Processing of experimental data; 6. Graphical handling of data with errors; 7. Fitting functions to data; 8. Back to Bayes: knowledge as a probability distribution; Answers to exercises; Part II. Appendices: A1. Combining uncertainties; A2. Systematic deviations due to random errors; A3. Characteristic function; A4. From binomial to normal distributions; A5. Central limit theorem; A6. Estimation of th varience; A7. Standard deviation of the mean; A8. Weight factors when variances are not equal; A11. Least squares fitting; Part III. Python codes; Part IV. Scientific data: Chi-squared distribution; F-distribution; Normal distribution; Physical constants; Probability distributions; Student's t-distribution; Units.

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