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 20
... sample distribution are not exactly repro- ducible from sample to sample . 2 . From the parameters of the sample probability distribution we can estimate the parameters of the parent probability distribution of the parent population of ...
... sample distribution are not exactly repro- ducible from sample to sample . 2 . From the parameters of the sample probability distribution we can estimate the parameters of the parent probability distribution of the parent population of ...
Page 335
... Sample standard deviations ) Sample covariance , 128 , 132 Sample mean , 18 , 24 Sample population , 17-19 Sample standard deviation s , variance s2 , 19 , 24 , 187-188 for arbitrary polynomial , 154 , 162 for linear function , 128 ...
... Sample standard deviations ) Sample covariance , 128 , 132 Sample mean , 18 , 24 Sample population , 17-19 Sample standard deviation s , variance s2 , 19 , 24 , 187-188 for arbitrary polynomial , 154 , 162 for linear function , 128 ...
Page 335
... Sample standard deviation s ) Sample covariance , 128 , 132 Sample mean , 18 , 24 Sample population , 17-19 Sample standard deviation s , variance s ?, 19 , 24 , 187-188 for arbitrary polynomial , 154 , 162 for linear function , 128 ...
... Sample standard deviation s ) Sample covariance , 128 , 132 Sample mean , 18 , 24 Sample population , 17-19 Sample standard deviation s , variance s ?, 19 , 24 , 187-188 for arbitrary polynomial , 154 , 162 for linear function , 128 ...
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Common terms and phrases
according to Equation AGAUSS ALPHA approximation ARRAY OF DATA average binomial distribution calculated CHI SQUARE CHISQR coins computer routine correlation counts/min curve defined degrees of freedom DELTAA derivatives DESCRIPTION OF PARAMETERS determine DOUBLE PRECISION equal error estimate evaluated in statement experiment experimental FCHISQ Figure fit the data fitting function fluctuations FORTRAN II FUNCTION SUBPROGRAMS REQUIRED FUNCTN Gaussian distribution given IMAX increments integral interval inverse inverse matrix LEAST-SQUARES FIT Legendre polynomial linear linear-correlation coefficient Lorentzian distribution matrix mean measurements minimum MODE MODIFICATIONS FOR FORTRAN NFREE NORDER NPTS NTERMS number of counts number of degrees NUMBER OF ITEMS parent distribution parent population peak Poisson distribution probability distribution probability function Program random result RETURN END sample SIGMAY square standard deviation statistical SUBROUTINES AND FUNCTION SUMX symmetric tion uncertainties value of x² variance WEIGHT(I YFIT YFIT(I yield δα σι στ дак