## Data Reduction and Error Analysis for the Physical SciencesThe purpose of this book is to provide an introduction to the concepts of statistical analysis of data for students at the undergraduate and graduate level, and to provide tools for data reduction and error analysis commonly required in the physical sciences. The presentation is developed from a practical point of view, including enough derivation to justify the results, but emphasizing methods of handling data more than theory. The text provides a variety of numerical and graphical techniques. Computer programs that support these techniques will be available on an accompanying website in both Fortran and C++. |

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This book is one of the few I've found in which the finer details of error analysis and statistics aren't "swept under the rug." It's an excellent book, and I've used it countless times since my first year as a Physics major.

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For over a decade this book has been my go-to source for methods of calculating uncertainty and for methods of non-linear model fitting. If I could only have a handful of technical books, this would be one of them.

### Contents

Probability Distributions | 17 |

Error Analysis | 36 |

Estimates of Mean and Errors | 51 |

Copyright | |

18 other sections not shown

### Other editions - View all

Data Reduction and Error Analysis for the Physical Sciences Philip R. Bevington,D. Keith Robinson No preview available - 2003 |

### Common terms and phrases

Appendix approximation assume background binomial distribution bins CALCCH ISQ calculated CH ISQR Chapter column consider correlation corresponding counts per minute data of Example data points data sample decay defined degrees of freedom determined digit ENDIF Equation error matrix estimate Example 6.2 experiment experimental factor fiducial Figure fitting function function y(x Gaussian distribution Gaussian function Gaussian probability graph histogram independent variable integral interpolation interval inverse inverse matrix kaon least-squares fit Legendre polynomials likelihood function linear linear-correlation coefficient Lorentzian maximum-likelihood mean and standard minimum Monte Carlo method nonlinear number of counts number of degrees number of events observations obtain parameters parent distribution parent population particles peak plot Poisson distribution PROB probability density probability density function probability distribution probability function problem procedure random numbers result RETURN END routines sē sē standard deviation starting values statistical Table tion uncertainties variance