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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Page 162
... solution of Equation ( 8.34 ) de- veloped from the Taylor expansion . If λ is very large , the diagonal terms of the cur- vature matrix dominate and the matrix equation degenerates into m separate equations β ; = λδα ; α ;; ( 8.40 ) ...
... solution of Equation ( 8.34 ) de- veloped from the Taylor expansion . If λ is very large , the diagonal terms of the cur- vature matrix dominate and the matrix equation degenerates into m separate equations β ; = λδα ; α ;; ( 8.40 ) ...
Page 244
... solution for Equation ( B.33 ) . For example , if two of the n simultaneous equa- tions are identical , except for a scale factor , there are really only n − 1 independent simultaneous equations , and therefore no solution for the n ...
... solution for Equation ( B.33 ) . For example , if two of the n simultaneous equa- tions are identical , except for a scale factor , there are really only n − 1 independent simultaneous equations , and therefore no solution for the n ...
Page 316
... solution , 116-121 independence of parameters , 127-135 Legendre polynomials , 132-134 matrix solution , 122–127 , 132 , 138 orthogonal polynomials , 129 spreadsheet use , 126–127 for straight line , 102-114 , 270 error estimation , 107 ...
... solution , 116-121 independence of parameters , 127-135 Legendre polynomials , 132-134 matrix solution , 122–127 , 132 , 138 orthogonal polynomials , 129 spreadsheet use , 126–127 for straight line , 102-114 , 270 error estimation , 107 ...
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Data Reduction and Error Analysis for the Physical Sciences Philip R. Bevington,D. Keith Robinson No preview available - 2003 |
Common terms and phrases
a₁ a₂ Appendix approximation assume binomial distribution bins CALCCHISQ calculated Chapter CHI2 CHISQR column correlation corresponding counts per minute data of Example data points data sample decay defined degrees of freedom determined digit ENDIF Equation error matrix estimate experiment experimental factor fiducial Figure fitting function function y(x Gaussian distribution Gaussian function Gaussian probability graph histogram independent variable integral interval inverse matrix kaon least-squares fit Legendre polynomials likelihood function linear linear-correlation coefficient mean and standard mean µ minimum nonlinear number of counts number of degrees number of events observations obtain parameters parent distribution parent population particles peak plot Poisson distribution polynomial probability density probability density function probability distribution probability function problem random numbers result RETURN END routines SAN DIEGO square standard deviation starting values statistical Table tion uncertainties v₁ v₂ value of x² variance x₁ y₁ σ² στ