Concise, practical guide in statistical methods for experimental data handling; ideal for course use and a handy reference for researchers.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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