code: def test_significance_discrete(self):
      nobs = 200
      np.random.seed(12345)
      ovals = np.random.binomial(2, 0.5, size=(nobs,))
      C2 = np.random.normal(2, 1, size=(nobs,))
      C3 = np.random.beta(0.5, 0.2, size=(nobs,))
      noise = np.random.normal(size=(nobs,))
      b1 = 1.2
      b2 = 3.7  # regression coefficients
      Y = b1 * ovals + b2 * C2 + noise
  
      bw = [3.63473198e00, 1.21404803e06]
      # This is the cv_ls bandwidth estimated earlier
      # The cv_ls bandwidth was estimated earlier to save time
      model = nparam.KernelReg(
          endog=[Y],
          exog=[ovals, C3],
          reg_type="ll",
          var_type="oc",
          bw=bw,
      )
      # This was also tested with local constant estimator
      nboot = 45  # Number of bootstrap samples
      sig_var1 = model.sig_test([0], nboot=nboot)  # H0: b1 = 0
      npt.assert_equal(sig_var1 == "Not Significant", False)
      sig_var2 = model.sig_test([1], nboot=nboot)  # H0: b2 = 0
      npt.assert_equal(sig_var2 == "Not Significant", True)
  
imports: import pytest
  import numpy
  import numpy.testing
  import statsmodels.api
label_desc: Use image node_id 11 for calling the TestKernelReg obj's underlying member method code with example usage: obj.test_significance_discrete() without return types