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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York :
Cambridge University Press,
©2011.
|
| Schriftenreihe: | Student guide series (Cambridge University Press)
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "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"-- |
| Beschreibung: | 1 online resource (xii, 225 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781139079822 1139079824 9781139077538 1139077538 9781139082099 1139082094 9780511921247 0511921241 1107083427 9781107083424 1283110903 9781283110907 1139075276 9781139075275 9786613110909 6613110906 1139069500 9781139069502 |
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| 245 | 1 | 2 | |a A student's guide to data and error analysis / |c Herman J.C. Berendsen. |
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| 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. | |
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| 650 | 7 | |a Error analysis (Mathematics) |2 fast | |
| 655 | 7 | |a dissertations. |2 aat | |
| 655 | 7 | |a Academic theses |2 fast | |
| 655 | 7 | |a Academic theses. |2 lcgft |0 http://id.loc.gov/authorities/genreForms/gf2014026039 | |
| 655 | 7 | |a Thèses et écrits académiques. |2 rvmgf | |
| 758 | |i has work: |a A student's guide to data and error analysis (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGHgYcG48RqbRpMVJVq8Fq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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| 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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Datensatz im Suchindex
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| author | Berendsen, Herman J. C. |
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| contents | 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. |
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| dewey-search | 511/.43 |
| dewey-sort | 3511 243 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-ocn729244732 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:17:51Z |
| institution | BVB |
| isbn | 9781139079822 1139079824 9781139077538 1139077538 9781139082099 1139082094 9780511921247 0511921241 1107083427 9781107083424 1283110903 9781283110907 1139075276 9781139075275 9786613110909 6613110906 1139069500 9781139069502 |
| language | English |
| oclc_num | 729244732 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xii, 225 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2011 |
| publishDateSearch | 2011 |
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| publisher | Cambridge University Press, |
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| series | Student guide series (Cambridge University Press) |
| spelling | Berendsen, Herman J. C. http://id.loc.gov/authorities/names/nb2007018229 A student's guide to data and error analysis / Herman J.C. Berendsen. Cambridge ; New York : Cambridge University Press, ©2011. 1 online resource (xii, 225 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file "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"-- Provided by publisher Includes bibliographical references and index. 880-01 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. Print version record. English. Error analysis (Mathematics) http://id.loc.gov/authorities/subjects/sh85044724 Mathematics. Théorie des erreurs. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh MATHEMATICS General. bisacsh Error analysis (Mathematics) fast dissertations. aat Academic theses fast Academic theses. lcgft http://id.loc.gov/authorities/genreForms/gf2014026039 Thèses et écrits académiques. rvmgf has work: A student's guide to data and error analysis (Text) https://id.oclc.org/worldcat/entity/E39PCGHgYcG48RqbRpMVJVq8Fq https://id.oclc.org/worldcat/ontology/hasWork Print version: Berendsen, Herman J.C. Student's guide to data and error analysis. Cambridge ; New York : Cambridge University Press, 2011 9780521119405 (DLC) 2010048231 (OCoLC)663441088 Student guide series (Cambridge University Press) http://id.loc.gov/authorities/names/no2022119099 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=366295 Volltext 505-01/(S 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. |
| spellingShingle | Berendsen, Herman J. C. A student's guide to data and error analysis / Student guide series (Cambridge University Press) 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. Error analysis (Mathematics) http://id.loc.gov/authorities/subjects/sh85044724 Mathematics. Théorie des erreurs. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh MATHEMATICS General. bisacsh Error analysis (Mathematics) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85044724 http://id.loc.gov/authorities/genreForms/gf2014026039 |
| title | A student's guide to data and error analysis / |
| title_auth | A student's guide to data and error analysis / |
| title_exact_search | A student's guide to data and error analysis / |
| title_full | A student's guide to data and error analysis / Herman J.C. Berendsen. |
| title_fullStr | A student's guide to data and error analysis / Herman J.C. Berendsen. |
| title_full_unstemmed | A student's guide to data and error analysis / Herman J.C. Berendsen. |
| title_short | A student's guide to data and error analysis / |
| title_sort | student s guide to data and error analysis |
| topic | Error analysis (Mathematics) http://id.loc.gov/authorities/subjects/sh85044724 Mathematics. Théorie des erreurs. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh MATHEMATICS General. bisacsh Error analysis (Mathematics) fast |
| topic_facet | Error analysis (Mathematics) Mathematics. Théorie des erreurs. TECHNOLOGY & ENGINEERING Engineering (General) MATHEMATICS General. dissertations. Academic theses Academic theses. Thèses et écrits académiques. |
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