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Everything You Wanted to Know About Data Analysis and Fitting but Were Afraid to Ask 2015 ed. [Mīkstie vāki]

  • Formāts: Paperback / softback, 85 pages, height x width: 235x155 mm, weight: 1591 g, 11 Illustrations, color; 4 Illustrations, black and white; X, 85 p. 15 illus., 11 illus. in color., 1 Paperback / softback
  • Sērija : SpringerBriefs in Physics
  • Izdošanas datums: 02-Jun-2015
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319190504
  • ISBN-13: 9783319190501
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  • Formāts: Paperback / softback, 85 pages, height x width: 235x155 mm, weight: 1591 g, 11 Illustrations, color; 4 Illustrations, black and white; X, 85 p. 15 illus., 11 illus. in color., 1 Paperback / softback
  • Sērija : SpringerBriefs in Physics
  • Izdošanas datums: 02-Jun-2015
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319190504
  • ISBN-13: 9783319190501
Citas grāmatas par šo tēmu:

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.

1 Introduction
1(2)
Reference
1(2)
2 Averages and Error Bars
3(18)
2.1 Basic Analysis
3(6)
2.2 Advanced Analysis
9(12)
2.2.1 Traditional Method
10(3)
2.2.2 Jackknife
13(2)
2.2.3 Bootstrap
15(4)
2.2.4 Jackknife or Bootstrap?
19(1)
References
19(2)
3 Fitting Data to a Model
21(34)
3.1 Fitting to a Straight Line
23(2)
3.2 Fitting to a Polynomial
25(1)
3.3 Error Bars
25(5)
3.4 Interpolating
30(2)
3.5 Fitting to a Non-linear Model
32(1)
3.6 Confidence Limits
32(4)
3.7 Confidence Limits by Resampling the Data
36(1)
3.8 A Tale of Two Probabilities. When Can One Rule Out a Fit?
37(2)
3.9 Model Selection (i.e. How to Avoid Over-Fitting): Maximum Likelihood Versus Bayes
39(16)
3.9.1 Maximum Likelihood
42(2)
3.9.2 A Bayesian Approach
44(8)
3.9.3 Conclusions for Model Selection
52(1)
References
53(2)
Appendix A Central Limit Theorem 55(6)
Appendix B The Number of Degrees of Freedom 61(2)
Appendix C The Chi-squared Distribution and the Goodness of Fit Parameter Q 63(4)
Appendix D Asymptotic Standard Error and How to Get Correct Error Bars from Gnuplot 67(4)
Appendix E The Distribution of Fitted Parameters Determined from Simulated Datasets 71(2)
Appendix F The Distribution of Fitted Parameters from Repeated Sets of Measurements 73(2)
Appendix G Fitting Correlated Data 75(2)
Appendix H Scripts for Some Data Analysis and Fitting Tasks 77
Research Professor of Physics. D. Phil., Oxford University 1973 Honor and Awards: 2014 Martin-Gutzwiller-Fellowship at the Max Planck Institute for the Physics of Complex Systems, Dresden 2012 Member of the American Academy of Arts and Sciences 2012 Humboldt Research Award (Humboldt Prize) of the Alexander von Humboldt Foundation 2009 Aneesur Rahman Prize of the American Physical Society for Computational Physics 2008 Outstanding Referee of the American Physical Society 2004-2005 Outstanding Faculty Award of the Division of Physical and Biological Sciences. 2003-2004 Excellence in Teaching Award of the University of California, Santa Cruz 1989, Fellow of the American Physical Society 1985 Maxwell Medal and Prize from the Institute of Physics (UK)