@wikipedia


\rho_{xy} = \frac{{\rm cov}(x,y)}{\sigma(x) \sigma(y)}

where

dynamic properties on the same discrete argument ()


{\rm cov}(x,y) = \frac{1}{n}  \sum_{i=1}^n (x_i - \bar x) (y_i - \bar y)



cross-property covariance


\sigma(x) = \sqrt { \frac{1}{n} \sum_{i=1}^n (x_i - \bar x)^2 }



standard deviation of property



\sigma(y) = \sqrt { \frac{1}{n} \sum_{i=1}^n (y_i - \bar y)^2 }



standard deviation of property


Pearson correlation coefficient ranges between -1 and 1 and indicates how close the two properties can be related by a linear correlation:

y_i = a x_i + b, \quad \forall \, i=1..N

with a certain pick on  and .