### Navigation

- index
- modules |
- next |
- previous |
- SciPy v0.7 Reference Guide (DRAFT) »
- Statistical functions (`scipy.stats`) »
- Statistical functions for masked arrays (`scipy.stats.mstats`) »

# 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 : 1D array y : 1D array the same length as x
  Returns: | (Pearson’s correlation coefficient, : 2-tailed p-value)

  References

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

### Navigation

- index
- modules |
- next |
- previous |
- SciPy v0.7 Reference Guide (DRAFT) »
- Statistical functions (`scipy.stats`) »
- Statistical functions for masked arrays (`scipy.stats.mstats`) »