Herman J. C. Berendsen

Student's Guide to Data and Error Analysis (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 7. April 2011
• Englisch
• ISBN-13: 9781139064927
• Artikelnr.: 38278938

Autorenporträt

Herman Berendsen is Emeritus Professor of Physical Chemistry at the University of Groningen, the Netherlands. His research started in nuclear magnetic resonance but focused later on molecular dynamics simulations on systems of biological interest. He is one of the pioneers in this field and, with over 35,000 citations, is one of the most quoted authors in physics and chemistry. He has taught courses in molecular modeling worldwide and authored the book Simulating the Physical World (Cambridge, 2007).

Inhaltsangabe

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.