code: def bw_scott(x, kernel=None):
      """
      Scott's Rule of Thumb
  
      Parameters
      ----------
      x : array_like
          Array for which to get the bandwidth
      kernel : CustomKernel object
          Unused
  
      Returns
      -------
      bw : float
          The estimate of the bandwidth
  
      Notes
      -----
      Returns 1.059 * A * n ** (-1/5.) where ::
  
         A = min(std(x, ddof=1), IQR/1.349)
         IQR = np.subtract.reduce(np.percentile(x, [75,25]))
  
      References
      ----------
  
      Scott, D.W. (1992) Multivariate Density Estimation: Theory, Practice, and
          Visualization.
      """
      A = _select_sigma(x)
      n = len(x)
      return 1.059 * A * n ** (-0.2)
  
file_path: statsmodels/statsmodels/nonparametric/bandwidths.py
imports: import numpy
  from scipy.stats import scoreatpercentile
  from statsmodels.compat.pandas import Substitution
  from statsmodels.sandbox.nonparametric import kernels
label_desc: Use image node_id 2 for calling a global function with example usage: bw_scott(x, kernel) and returns: unknown