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Goodness of fit
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-squared 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.
==Fit of distributions==
In assessing whether a given distribution is suited to a data-set, the following tests and their underlying measures of fit can be used:
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*Kolmogorov–Smirnov test;
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*Cramér–von Mises criterion;
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*Anderson–Darling test;
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*Shapiro–Wilk test;
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*Chi Square test;
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*Akaike information criterion;
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*Hosmer–Lemeshow test;
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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