GLOSSARY

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LaTeX Math Inline
bodyx = \{ x_1, \, x_2, \, x_3 , ... x_N \}

observed  variable represented by a discrete data set of numerical samples

LaTeX Math Inline
body\hat x = \{ \hat x_1, \, \hat x_2, \, \hat x_3 , ... \hat x_N \}

predictor of variable 

LaTeX Math Inline
bodyx
, represented by another discrete data set of numerical samples,

with the same number of samples 

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

LaTeX Math Inline
body--uriencoded--\bar x = \frac%7B1%7D%7BN%7D \sum_i x_i

mean value of the variable 

LaTeX Math Inline
bodyx
, which can be considered as some sort of extreme predictor with zero variability

LaTeX Math Inline
bodyMSD(x, \hat x)

mean square deviation between a variable 

LaTeX Math Inline
bodyx
 and its predictor 
LaTeX Math Inline
body\hat x

LaTeX Math Inline
bodyMSD(x, \bar x)

mean square deviation between a variable 

LaTeX Math Inline
bodyx
 and its mean value 
LaTeX Math Inline
body\bar x


It is similar to Mean Square Deviation (MSD) but quantifies the model prediction efficiency in normalized way which sometimes is normally more suitable for computationsassessment goodness of fit.


The coefficient of determination  

LaTeX Math Inline
bodyR^2
 normally ranges between :

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