Sök i LIBRIS databas



Sökning: onr:18126683 > Everything You Want...

Everything You Wanted to Know About Data Analysis and Fitting but Were Afraid to Ask [Elektronisk resurs] / by Peter Young.

Young, Peter (författare)
SpringerLink (Online service) 
ISBN 9783319190518
Publicerad: Cham : Springer, 2015
Engelska online resource (x, 85 s.)
Serie: SpringerBriefs in Physics, 2191-5431
  • E-bok
Innehållsförteckning Sammanfattning Ämnesord
  • 1 Introduction -- 2 Averages and Error Bars -- 2.1 Basic Analysis -- 2.2 Advanced Analysis -- 2.2.1 Traditional Method -- 2.2.2 Jackknife -- 2.2.3 Bootstrap -- 2.2.4 Jackknife or Bootstrap? -- 3 Fitting Data to a Model -- 3.1 Fitting to a Straight Line -- 3.2 Fitting to a Polynomial -- 3.3 Error Bars -- 3.4 Interpolating -- 3.5 Fitting to a Non-linear Model -- 3.6 Confidence Limits -- 3.7 Confidence Limits by Resampling the Data -- 3.8 A Tale of Two Probabilities. When Can One Rule Out a Fit? -- 3.9 Model Selection (i.e. How to Avoid Over-Fitting): Maximum Likelihood Versus Bayes -- 3.9.1 Maximum Likelihood -- 3.9.2 A Bayesian Approach -- 3.9.3 Conclusions for Model Selection -- Appendix A: Central Limit Theorem -- Appendix B: The Number of Degrees of Freedom -- Appendix C: The Chi-squared Distribution and the Goodness of Fit Parameter Q -- Appendix D: Asymptotic Standard Error and How to Get Correct Error Bars from Gnuplot -- Appendix E: The Distribution of Fitted Parameters Determined from Simulated Datasets -- Appendix F: The Distribution of Fitted Parameters from Repeated Sets of Measurements -- Appendix G: Fitting Correlated Data -- Appendix H: Scripts for Some Data Analysis and Fitting Tasks. 
  • These notes describe how to average and fit numerical data that have been obtained either by simulation or measurement. Following an introduction on how to estimate various average values, they discuss how to determine error bars on those estimates, and how to proceed for combinations of measured values. Techniques for fitting data to a given set of models will be described in the second part of these notes. This primer equips readers to properly derive the results covered, presenting the content in a style suitable for a physics audience. It also includes scripts in python, perl and gnuplot for performing a number of tasks in data analysis and fitting, thereby providing readers with a useful reference guide. 


Mathematical physics  (LCSH)
Maximal subgroups  (LCSH)
Numerical analysis  (LCSH)
Physics  (LCSH)
Measurement Science and Instrumentation 
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences 
Numerical and Computational Physics 


T50 (LCC)
PDDM (ämneskategori)
SCI068000 (ämneskategori)
530.8 (DDC)
Tf (kssb/8 (machine generated))
Inställningar Hjälp

Titeln finns på 6 bibliotek. 

Bibliotek i Stockholmsregionen (2)

Ange som favorit

Bibliotek i västra Sverige (1)

Ange som favorit

Bibliotek i södra Sverige (3)

Ange som favorit
Fel i posten?
Teknik och format
Sök utifrån
LIBRIS söktjänster

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

Copyright © LIBRIS - Nationella bibliotekssystem

pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy