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S_3 = \bar \hatmu_3(\delta x) = \bar \mu_3(x-\hat x)

where where

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body--uriencoded--x = \{ %7B x_1, \, x_2, \, x_3 , ... x_N \}%7D

observed  variable represented by a discrete  dataset of numerical samples

numerical dataset, representing the samples of property 

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bodyx

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body--uriencoded--\hat x = \{ %7B \hat x_1, \, \hat x_2, \, \hat x_3 , ... \hat x_N \}%7D

model prediction of property 

predictor of variable 

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bodyx
, represented by another discrete  dataset of numerical samples,

with the same number of samples 

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bodyN
 predicted at the same conditions as the original samples 
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body \{ x_1, \, x_2, \, x_3 , ... x_N \}

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body\delta x = x - \hat x

discrepancy dataset

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body\bar \mu_3(\delta x)

3-rd order standardized central momentum of the discrepancy dataset 

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body\delta x



See also

...

Natural Human / Science / Formal Science / Mathematics / System / Model / Model Validation / Goodness of fit

Formal science / Mathematics / Statistics Statistical correlation / Correlation coefficient

...