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# scipy.stats.mstats.linregress

`scipy.stats.mstats.``linregress`(*args)

  > Calculate a regression line

  > This computes a least-squares regression for two sets of measurements.

  Parameters : | x, y : array_like two sets of measurements. Both arrays should have the same length. If only x is given (and y=None), then it must be a two-dimensional array where one dimension has length 2. The two sets of measurements are then found by splitting the array along the length-2 dimension.
  Returns : | slope : float slope of the regression line intercept : float intercept of the regression line r-value : float correlation coefficient p-value : float two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero. stderr : float Standard error of the estimate

  Notes

  Missing values are considered pair-wise: if a value is missing in x, the corresponding value in y is masked.

  Examples

      >>> from scipy import stats
      >>> import numpy as np
      >>> x = np.random.random(10)
      >>> y = np.random.random(10)
      >>> slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)

  # To get coefficient of determination (r_squared)

      >>> print "r-squared:", r_value**2
      r-squared: 0.15286643777

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