Correlation¶

gsl_stats_correlation(data1, data2)¶

This function efficiently computes the Pearson correlation coefficient between the datasets data1 and data2 which must both be of the same length n.

\[r = {cov(x, y) \over \hat{\sigma_x} \hat{\sigma_y}} = {{1 \over n-1} \sum (x_i - \hat{x}) (y_i - \hat{y}) \over \sqrt{{1 \over n-1} \sum (x_i - {\hat{x}})^2} \sqrt{{1 \over n-1} \sum (y_i - {\hat{y}})^2} }\]

gsl_stats_spearman(data1, data2)¶

This function computes the Spearman rank correlation coefficient between the datasets data1 and data2 which must both be of the same length n. The Spearman rank correlation between vectors \(x\) and \(y\) is equivalent to the Pearson correlation between the ranked vectors \(x_R\) and \(y_R\), where ranks are defined to be the average of the positions of an element in the ascending order of the values.

Covariance

Maximum and Minimum values