code: def test_ci_skew(self):
      # This will be tested in a round about way since MATLAB fails when
      # computing CI with multiple nuisance parameters.  The process is:
      #
      # (1) Get CI for skewness from ci.skew()
      # (2) In MATLAB test the hypotheis that skew=results of test_skew.
      # (3) If p-value approx .05, test confirmed
      skew_ci = self.res1.ci_skew()
      lower_lim = skew_ci[0]
      upper_lim = skew_ci[1]
      ul_pval = self.res1.test_skew(lower_lim)[1]
      ll_pval = self.res1.test_skew(upper_lim)[1]
      assert_almost_equal(ul_pval, 0.050000, 4)
      assert_almost_equal(ll_pval, 0.050000, 4)
  
imports: import numpy
  from numpy.testing import assert_almost_equal
  from statsmodels.datasets import star98
  from statsmodels.emplike.descriptive import DescStat
  from .results.el_results import DescStatRes
label_desc: Use image node_id 11 for calling the TestDescriptiveStatistics obj's underlying member method code with example usage: obj.test_ci_skew() without return types