# scipy.stats.mstats.pearsonr

scipy.stats.mstats.pearsonr(x, y)

  Calculates a Pearson correlation coefficient and the p-value for testing non-correlation.

  The Pearson correlation coefficient measures the linear relationship between two datasets. Strictly speaking, Pearson’s correlation requires that each dataset be normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.

  The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. The p-values are not entirely reliable but are probably reasonable for datasets larger than 500 or so.

  Parameters
    x 1-D array_like

      Input

    y 1-D array_like

      Input

  Returns
    pearsonr float

      Pearson’s correlation coefficient, 2-tailed p-value.

  References

  http://www.statsoft.com/textbook/glosp.html#Pearson%20Correlation

scipy.stats.mstats.f_oneway scipy.stats.mstats.spearmanr