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: Significance test for the variables in the regression.
  
  Parameters
  ----------
  var_pos : sequence
      The position of the variable in exog to be tested.
  
  Returns
  -------
  sig : str
      The level of significance:
  
          - `*` : at 90% confidence level
          - `**` : at 95% confidence level
          - `***` : at 99* confidence level
          - "Not Significant" : if not significant
code: def sig_test(self, var_pos, nboot=50, nested_res=25, pivot=False):
      """
      Significance test for the variables in the regression.
  
      Parameters
      ----------
      var_pos : sequence
          The position of the variable in exog to be tested.
  
      Returns
      -------
      sig : str
          The level of significance:
  
              - `*` : at 90% confidence level
              - `**` : at 95% confidence level
              - `***` : at 99* confidence level
              - "Not Significant" : if not significant
      """
      var_pos = np.asarray(var_pos)
      ix_cont, ix_ord, ix_unord = _get_type_pos(self.var_type)
      if np.any(ix_cont[var_pos]):
          if np.any(ix_ord[var_pos]) or np.any(ix_unord[var_pos]):
              raise ValueError(
                  "Discrete variable in hypothesis. Must be continuous"
              )
  
          Sig = TestRegCoefC(self, var_pos, nboot, nested_res, pivot)
      else:
          Sig = TestRegCoefD(self, var_pos, nboot)
  
      return Sig.sig
  
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 9 for calling the KernelReg obj's underlying member method code with example usage: obj.sig_test(var_pos, nboot, nested_res, pivot) and returns: Sig