rcept, low_slope, high_slope = theilslopes(y, x)

References
----------
.. [1] P.K. Sen, "Estimates of the regression coefficient based on
       Kendall's tau", J. Am. Stat. Assoc., Vol. 63, pp. 1379-1389, 1968.
.. [2] H. Theil, "A rank-invariant method of linear and polynomial
       regression analysis I, II and III",  Nederl. Akad. Wetensch., Proc.
       53:, pp. 386-392, pp. 521-525, pp. 1397-1412, 1950.
.. [3] W.L. Conover, "Practical nonparametric statistics", 2nd ed.,
       John Wiley and Sons, New York, pp. 493.
.. [4] https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator

Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> import matplotlib.pyplot as plt

>>> x = np.linspace(-5, 5, num=150)
>>> y = x + np.random.normal(size=x.size)
>>> y[11:15] += 10  # add outliers
>>> y[-5:] -= 7

Compute the slope, intercept and 90% confidence interval.  For comparison,
also compute the least-squares fit with `linregress`:

>>> res = stats.theilslopes(y, x, 0.90, method='separate')
>>> lsq_res = stats.linregress(x, y)

Plot the results. The Theil-Sen regression line is shown in red, with the
dashed red lines illustrating the confidence interval of the slope (note
that the dashed red lines are not the confidence interval of the regression
as the confidence interval of the intercept is not included). The green
line shows the least-squares fit for comparison.

>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.plot(x, y, 'b.')
>>> ax.plot(x, res[1] + res[0] * x, 'r-')
>>> ax.plot(x, res[1] + res[2] * x, 'r--')
>>> ax.plot(x, res[1] + res[3] * x, 'r--')
>>> ax.plot(x, lsq_res[1] + lsq_res[0] * x, 'g-')
>>> plt.show()

)