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A student's guide to data and error analysis /
"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 i...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge ; New York :
Cambridge University Press,
©2011.
|
| Colección: | Student guide series (Cambridge University Press)
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Berendsen, Herman J. C. | |
| 245 | 1 | 2 | |a A student's guide to data and error analysis / |c Herman J.C. Berendsen. |
| 260 | |a Cambridge ; |a New York : |b Cambridge University Press, |c ©2011. | ||
| 300 | |a 1 online resource (xii, 225 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 520 | |a "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"-- |c Provided by publisher | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |6 880-01 |a Data and error analysis. Introduction -- The presentation of physical quantities with their inaccuracies -- Errors: classification and propagation -- Probability distributions -- Processing of experimental data -- Graphical handling of data with errors -- Fitting functions to data -- Back to Bayes: knowledge as a probability distribution -- Answers to exercises --Appendices. Combining uncertainties -- Systematic deviations due to random errors -- Characteristic function -- From binomial to normal distributions -- Central limit theorem -- Estimation of th varience -- Standard deviation of the mean -- Weight factors when variances are not equal -- Least squares fitting -- Python codes -- Scientific data. | |
| 588 | 0 | |a Print version record. | |
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Error analysis (Mathematics) | |
| 650 | 4 | |a Mathematics. | |
| 650 | 6 | |a Théorie des erreurs. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Engineering (General) |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x General. |2 bisacsh | |
| 650 | 7 | |a Error analysis (Mathematics) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Berendsen, Herman J.C. |t Student's guide to data and error analysis. |d Cambridge ; New York : Cambridge University Press, 2011 |z 9780521119405 |w (DLC) 2010048231 |w (OCoLC)663441088 |
| 830 | 0 | |a Student guide series (Cambridge University Press) | |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=366295 |z Texto completo |
| 880 | 8 | |6 505-01/(S |a Updating knowledge: Avogadro's number -- Inference from a series of normally distributed samples -- Infer a rate constant from a few events -- 8.5 Conclusion -- References -- Answers to exercises -- Part II: Appendices -- A1 Combining uncertainties -- Why do squared uncertainties add up in sums-- A2 Systematic deviations due to random errors -- A special case: sampling exponential functions -- A3 Characteristic function -- A4 From binomial to normal distributions -- A4.1 The binomial distribution -- A4.2 The multinomial distribution -- A4.3 The Poisson distribution -- From binomial to Poisson -- Properties of the Poisson distribution -- A4.4 The normal distribution -- From Poisson to normal -- A5 Central limit theorem -- A6 Estimation of the variance -- Why is the best estimate for the variance larger than the mean squared deviation of the average-- Uncorrelated data points -- Correlated data points -- A7 Standard deviation of the mean -- Why is the variance of the mean of n independent data equal to the variance of x itself divided by n-- How is this result influenced when the data are correlated-- Example -- How accurate is the estimated standard deviation-- A8 Weight factors when variances are not equal -- What is the "best" determination of the mean of a number of data xi with the same expectations μ but with unequal standard deviations σi-- How large is the variance in -- A9 Least-squares fitting -- A9.1 How do you find the best parameters a and b in y approx ax + b-- A9.2 General linear regression -- A9.3 SSQ as a function of the parameters -- A9.4 Covariances of the parameters -- Why is the s.d. of a parameter given by the projection of the ellipsoid ... -- Nonlinear least-squares fit -- Part III: Python codes -- Part IV: Scientific data -- Chi-squared distribution -- Probability distribution sum of squares. | |
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