A number characterising the prediction quality ( goodness of fit ):

R^2 = 1 - \frac{MSE(x, \hat x)}{MSE(x, \bar x)}, \quad 0 \leq R^2 \leq 1

where

	a variable represented by a discrete data set of numerical samples
	predictor of variable , represented by another discrete data set of numerical samples, with the same number of samples predicted at the same conditions as the original samples
	mean value of the variable , which can be considered as some sort of extreme predictor with zero variability
	mean square error between a variable and its predictor
	mean square error between a variable and its mean value

The coefficient of determination normally ranges between 0, indicating a poor fit and 1, indicating a good fit.

See also