# scipy.stats.mstats.siegelslopes

scipy.stats.mstats.siegelslopes(y, x=None, method='hierarchical')

  Computes the Siegel estimator for a set of points (x, y).

  `siegelslopes` implements a method for robust linear regression using repeated medians to fit a line to the points (x, y). The method is robust to outliers with an asymptotic breakdown point of 50%.

  Parameters
    y array_like

      Dependent variable.

    x array_like or None, optional

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

    method{‘hierarchical’, ‘separate’}

      If ‘hierarchical’, estimate the intercept using the estimated slope `medslope` (default option). If ‘separate’, estimate the intercept independent of the estimated slope. See Notes for details.

  Returns
    medslope float

      Estimate of the slope of the regression line.

    medintercept float

      Estimate of the intercept of the regression line.

  See also

  `theilslopes`

    a similar technique without repeated medians

  Notes

  For more details on `siegelslopes`, see `scipy.stats.siegelslopes`.

scipy.stats.mstats.linregress scipy.stats.mstats.theilslopes