Fitting models to correlated data III: A comparison between residual analysis and other methods |
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Authors: | Jean-Louis Fé mé nias |
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Affiliation: | Institut Non Linéaire de Nice, UMR 6618 CNRS, Université de Nice, Sophia-Antipolis Sophia-Antipolis, 1361 route des Lucioles, 06560 Valbonne, France |
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Abstract: | Applications of the χ2 test, the F test, the Durbin-Watson d test, and the f (or Sign) test, to examples of correlated data treatment, show important drawbacks with the d test and (apparently) with the f test. An analytical approach based on residual analysis suggests an improvement in their use that leads to better results at lowest order; it also points out a distinction between goodness-of-fit tests, as the f test, and goodness-of-modeling tests, as the χ2 and F tests. The residual analysis method is applied to the same examples; it looks faster, simpler, and often more accurate than the classical ones. |
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Keywords: | Chi-square test Correlated data Durbin-Watson test Experimental errors Fisher-Sné decor test Fitting to data Goodness of fit Goodness of modeling Least-squares Model error Residuals of fit Sign test |
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