Herman J. C. Berendsen
A Student's Guide to Data and Error Analysis
Herman J. C. Berendsen
A Student's Guide to Data and Error Analysis
- Gebundenes Buch
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
Concise, practical guide in statistical methods for experimental data handling; ideal for course use and a handy reference for researchers.
Andere Kunden interessierten sich auch für
- Henry MortonThe Student's Practical Chemistry: A Text-Book On Chemical Physics and Inorganic and Organic Chemistry38,99 €
- Colin TissenThe Effects of Monetary Policy in the US. The Vector Error Correction Model (VECM) compared to the Structural Autoregressive Model (SVAR)17,95 €
- Vera PlessIntroduction to the Theory of Error-Correcting Codes219,99 €
- Gilbert StrangIntroduction to Applied Mathematics93,99 €
- Louis LyonsAll You Wanted to Know about Mathematics But Were Afraid to Ask184,99 €
- Wouter de NooyExploratory Social Network Analysis with Pajek130,99 €
- Anatoly N. KochubeiAnalysis in Positive Characteristic116,99 €
-
-
-
Concise, practical guide in statistical methods for experimental data handling; ideal for course use and a handy reference for researchers.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 238
- Erscheinungstermin: 15. März 2012
- Englisch
- Abmessung: 235mm x 157mm x 19mm
- Gewicht: 545g
- ISBN-13: 9780521119405
- ISBN-10: 0521119405
- Artikelnr.: 31768145
- Verlag: Cambridge University Press
- Seitenzahl: 238
- Erscheinungstermin: 15. März 2012
- Englisch
- Abmessung: 235mm x 157mm x 19mm
- Gewicht: 545g
- ISBN-13: 9780521119405
- ISBN-10: 0521119405
- Artikelnr.: 31768145
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).
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.
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.
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.