# scipy.stats.mstats.theilslopes

scipy.stats.mstats.theilslopes(y, x=None, alpha=0.95)

  Computes the Theil-Sen estimator for a set of points (x, y).

  `theilslopes` implements a method for robust linear regression. It computes the slope as the median of all slopes between paired values.

  Parameters
    y array_like

      Dependent variable.

    x array_like or None, optional

      Independent variable. If None, use `arange(len(y))` instead.

    alpha float, optional

      Confidence degree between 0 and 1. Default is 95% confidence. Note that alpha is symmetric around 0.5, i.e. both 0.1 and 0.9 are interpreted as “find the 90% confidence interval”.

  Returns
    medslope float

      Theil slope.

    medintercept float

      Intercept of the Theil line, as `median(y) - medslope*median(x)`.

    lo_slope float

      Lower bound of the confidence interval on medslope.

    up_slope float

      Upper bound of the confidence interval on medslope.

  See also

  `siegelslopes`

    a similar technique with repeated medians

  Notes

  For more details on `theilslopes`, see stats.theilslopes.

scipy.stats.mstats.siegelslopes scipy.stats.mstats.sen_seasonal_slopes