A Student's Guide to Data and Error Analysis

Herman J. C. Berendsen (Rijksuniversiteit Groningen, The Netherlands)

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English

07 April 2011

Mathematics & Sciences; Mathematical foundations; Probability & statistics; Maths for scientists; Maths for engineers

Series: Student's Guides

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.

By: Herman J. C. Berendsen (Rijksuniversiteit Groningen The Netherlands)
Country of Publication: United Kingdom
Dimensions: Height: 226mm, Width: 152mm, Spine: 13mm
Weight: 380g
ISBN: 9780521134927
ISBN 10: 0521134927
Series: Student's Guides
Publication Date: 07 April 2011
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Paperback
Publisher's Status: Active

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 the varience; A7. Standard deviation of the mean; A8. Weight factors when variances are not equal; A9. 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.

Reviews for A Student's Guide to Data and Error Analysis

"""Overall, this would be a nice text or reference to accompany a short course in statistics for undergraduate science or engineering..also useful for researchers desiring a primer or review...Recommended."" - CHOICE"