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# scipy.stats.mstats.linregress

`scipy.stats.mstats.``linregress`(x, y=None)

  Calculate a linear 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 rvalue : float correlation coefficient pvalue : float two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero. stderr : float Standard error of the estimated gradient.

  See also

  `scipy.optimize.curve_fit`
    Use non-linear least squares to fit a function to data.
  `scipy.optimize.leastsq`
    Minimize the sum of squares of a set of equations.

  Notes

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

  Examples

      >>> import matplotlib.pyplot as plt
      >>> from scipy import stats
      >>> np.random.seed(12345678)
      >>> 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:', 0.080402268539028335)

  Plot the data along with the fitted line

      >>> plt.plot(x, y, 'o', label='original data')
      >>> plt.plot(x, intercept + slope*x, 'r', label='fitted line')
      >>> plt.legend()
      >>> plt.show()

  (Source code)