class_docstr: Nonparametric kernel regression class.
  
  Calculates the conditional mean ``E[y|X]`` where ``y = g(X) + e``.
  Note that the "local constant" type of regression provided here is also
  known as Nadaraya-Watson kernel regression; "local linear" is an extension
  of that which suffers less from bias issues at the edge of the support. Note
  that specifying a custom kernel works only with "local linear" kernel
  regression. For example, a custom ``tricube`` kernel yields LOESS regression.
  
  Parameters
  ----------
  endog : array_like
      This is the dependent variable.
  exog : array_like
      The training data for the independent variable(s)
      Each element in the list is a separate variable
  var_type : str
      The type of the variables, one character per variable:
  
          - c: continuous
          - u: unordered (discrete)
          - o: ordered (discrete)
  
  reg_type : {'lc', 'll'}, optional
      Type of regression estimator. 'lc' means local constant and
      'll' local Linear estimator.  Default is 'll'
  bw : str or array_like, optional
      Either a user-specified bandwidth or the method for bandwidth
      selection. If a string, valid values are 'cv_ls' (least-squares
      cross-validation) and 'aic' (AIC Hurvich bandwidth estimation).
      Default is 'cv_ls'. User specified bandwidth must have as many
      entries as the number of variables.
  ckertype : str, optional
      The kernel used for the continuous variables.
  okertype : str, optional
      The kernel used for the ordered discrete variables.
  ukertype : str, optional
      The kernel used for the unordered discrete variables.
  defaults : EstimatorSettings instance, optional
      The default values for the efficient bandwidth estimation.
  
  Attributes
  ----------
  bw : array_like
      The bandwidth parameters.
docstr: Computes the measure of dispersion.
  
  The minimum of the standard deviation and interquartile range / 1.349
  
  References
  ----------
  See the user guide for the np package in R.
  In the notes on bwscaling option in npreg, npudens, npcdens there is
  a discussion on the measure of dispersion
code: def _compute_dispersion(self, data):
      """
      Computes the measure of dispersion.
  
      The minimum of the standard deviation and interquartile range / 1.349
  
      References
      ----------
      See the user guide for the np package in R.
      In the notes on bwscaling option in npreg, npudens, npcdens there is
      a discussion on the measure of dispersion
      """
      data = data[:, 1:]
      return _compute_min_std_IQR(data)
  
imports: import copy
  import numpy
  from scipy import optimize
  from scipy.stats.mstats import mquantiles
  from ._kernel_base import GenericKDE, EstimatorSettings, gpke, LeaveOneOut, _get_type_pos, _adjust_shape, _compute_min_std_IQR, kernel_func
label_desc: Use image node_id 12 for calling the KernelReg obj's underlying member method code with example usage: obj._compute_dispersion(data) and returns: _compute_min_std_IQR