code: def test_significance_continuous(self):
      nobs = 250
      np.random.seed(12345)
      C1 = np.random.normal(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 * C1 + b2 * C2 + noise
  
      # This is the cv_ls bandwidth estimated earlier
      bw = [11108137.1087194, 1333821.85150218]
      model = nparam.KernelReg(
          endog=[Y], exog=[C1, C3], reg_type="ll", var_type="cc", bw=bw
      )
      nboot = 45  # Number of bootstrap samples
      sig_var12 = model.sig_test(
          [0, 1], nboot=nboot
      )  # H0: b1 = 0 and b2 = 0
      npt.assert_equal(sig_var12 == "Not Significant", False)
      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 10 for calling the TestKernelReg obj's underlying member method code with example usage: obj.test_significance_continuous() without return types