class_docstr: Significance test for the categorical variables in a nonparametric
  regression.
  
  Parameters
  ----------
  model : Instance of KernelReg class
      This is the nonparametric regression model whose elements
      are tested for significance.
  test_vars : tuple, list of one element
      index of position of the discrete variable to be tested
      for significance. E.g. (3) tests variable at
      position 3 for significance.
  nboot : int
      Number of bootstrap samples used to determine the distribution
      of the test statistic in a finite sample. Default is 400
  
  Attributes
  ----------
  sig : str
      The significance level of the variable(s) tested
      "Not Significant": Not significant at the 90% confidence level
                          Fails to reject the null
      "*": Significant at the 90% confidence level
      "**": Significant at the 95% confidence level
      "***": Significant at the 99% confidence level
  
  Notes
  -----
  This class currently does not allow joint hypothesis.
  Only one variable can be tested at a time
  
  References
  ----------
  See [9] and chapter 12 in [1].
docstr: Calculates the expected conditional mean
  m(X, Z=l) for all possible l
code: def _est_cond_mean(self):
      """
      Calculates the expected conditional mean
      m(X, Z=l) for all possible l
      """
      self.dom_x = np.sort(np.unique(self.exog[:, self.test_vars]))
      X = copy.deepcopy(self.exog)
      m = 0
      for i in self.dom_x:
          X[:, self.test_vars] = i
          m += self.model.fit(data_predict=X)[0]
  
      m = m / float(len(self.dom_x))
      m = np.reshape(m, (np.shape(self.exog)[0], 1))
      return m
  
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 3 for calling the TestRegCoefD obj's underlying member method code with example usage: obj._est_cond_mean() and returns: m