國立虎尾科技大學 |

A student's guide to data and error analysis /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A student's guide to data and error analysis // Herman J.C. Berendsen.
remainder title:	A Student's Guide to Data & Error Analysis
Author:	Berendsen, Herman J. C.,
Description:	1 online resource (xii, 225 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Subject:	Error analysis (Mathematics) -
Online resource:	https://doi.org/10.1017/CBO9780511921247
ISBN:	9780511921247 (ebook)

A student's guide to data and error analysis /
Berendsen, Herman J. C.,

A student's guide to data and error analysis /A Student's Guide to Data & Error AnalysisHerman J.C. Berendsen. - 1 online resource (xii, 225 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

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; A11. Least squares fitting -- Part III. Python codes -- Part IV. Scientific data.

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.

ISBN: 9780511921247 (ebook)Subjects--Topical Terms:

527853
Error analysis (Mathematics)

LC Class. No.: QA275 / .B43 2011

Dewey Class. No.: 511/.43

A student's guide to data and error analysis /
LDR:02650nam a2200313 i 4500 001 1127564
003 UkCbUP
005 20151005020622.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 240926s2011||||enk o ||1 0|eng|d
020 $a 9780511921247 (ebook)
020 $z 9780521119405 (hardback)
020 $z 9780521134927 (paperback)
035 $a CR9780511921247
040 $a UkCbUP $b eng $e rda $c UkCbUP
050 0 0 $a QA275 $b .B43 2011
082 0 0 $a 511/.43 $2 22
100 1 $a Berendsen, Herman J. C., $e author. $3 1447019
245 1 2 $a A student's guide to data and error analysis / $c Herman J.C. Berendsen.
246 3 $a A Student's Guide to Data & Error Analysis
264 1 $a Cambridge : $b Cambridge University Press, $c 2011.
300 $a 1 online resource (xii, 225 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Title from publisher's bibliographic system (viewed on 05 Oct 2015).
505 0 $a 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; A11. Least squares fitting -- Part III. Python codes -- Part IV. Scientific data.
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.
650 0 $a Error analysis (Mathematics) $3 527853
776 0 8 $i Print version: $z 9780521119405
856 4 0 $u https://doi.org/10.1017/CBO9780511921247