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Concept
Qualité de l'ajustement
Résumé
The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e.g. to test for normality of residuals, to test whether two samples are drawn from identical distributions (see Kolmogorov–Smirnov test), or whether outcome frequencies follow a specified distribution (see Pearson's chi-square test). In the analysis of variance, one of the components into which the variance is partitioned may be a lack-of-fit sum of squares.
In assessing whether a given distribution is suited to a data-set, the following tests and their underlying measures of fit can be used:
Bayesian information criterion
Kolmogorov–Smirnov test
Cramér–von Mises criterion
Anderson–Darling test
Berk-Jones tests
Shapiro–Wilk test
Chi-squared test
Akaike information criterion
Hosmer–Lemeshow test
Kuiper's test
Kernelized Stein discrepancy
Zhang's ZK, ZC and ZA tests
Moran test
Density Based Empirical Likelihood Ratio tests
In regression analysis, more specifically regression validation, the following topics relate to goodness of fit:
Coefficient of determination (the R-squared measure of goodness of fit);
Lack-of-fit sum of squares;
Mallows's Cp criterion
Prediction error
Reduced chi-square
The following are examples that arise in the context of categorical data.
Pearson's chi-square test uses a measure of goodness of fit which is the sum of differences between observed and expected outcome frequencies (that is, counts of observations), each squared and divided by the expectation:
where:
Oi = an observed count for bin i
Ei = an expected count for bin i, asserted by the null hypothesis.
The expected frequency is calculated by:
where:
F = the cumulative distribution function for the probability distribution being tested.
Yu = the upper limit for class i,
Yl = the lower limit for class i, and
N = the sample size
The resulting value can be compared with a chi-square distribution to determine the goodness of fit.
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