code: def compare_cox(results_x, results_z, store=False):
      """
      Compute the Cox test for non-nested models
  
      Parameters
      ----------
      results_x : Result instance
          result instance of first model
      results_z : Result instance
          result instance of second model
      store : bool, default False
          If true, then the intermediate results are returned.
  
      Returns
      -------
      tstat : float
          t statistic for the test that including the fitted values of the
          first model in the second model has no effect.
      pvalue : float
          two-sided pvalue for the t statistic
      res_store : ResultsStore, optional
          Intermediate results. Returned if store is True.
  
      Notes
      -----
      Tests of non-nested hypothesis might not provide unambiguous answers.
      The test should be performed in both directions and it is possible
      that both or neither test rejects. see [1]_ for more information.
  
      Formulas from [1]_, section 8.3.4 translated to code
  
      Matches results for Example 8.3 in Greene
  
      References
      ----------
      .. [1] Greene, W. H. Econometric Analysis. New Jersey. Prentice Hall;
         5th edition. (2002).
      """
      if _check_nested_results(results_x, results_z):
          raise ValueError(NESTED_ERROR.format(test="Cox comparison"))
      x = results_x.model.exog
      z = results_z.model.exog
      nobs = results_x.model.endog.shape[0]
      sigma2_x = results_x.ssr / nobs
      sigma2_z = results_z.ssr / nobs
      yhat_x = results_x.fittedvalues
      res_dx = OLS(yhat_x, z).fit()
      err_zx = res_dx.resid
      res_xzx = OLS(err_zx, x).fit()
      err_xzx = res_xzx.resid
  
      sigma2_zx = sigma2_x + np.dot(err_zx.T, err_zx) / nobs
      c01 = nobs / 2.0 * (np.log(sigma2_z) - np.log(sigma2_zx))
      v01 = sigma2_x * np.dot(err_xzx.T, err_xzx) / sigma2_zx**2
      q = c01 / np.sqrt(v01)
      pval = 2 * stats.norm.sf(np.abs(q))
  
      if store:
          res = ResultsStore()
          res.res_dx = res_dx
          res.res_xzx = res_xzx
          res.c01 = c01
          res.v01 = v01
          res.q = q
          res.pvalue = pval
          res.dist = stats.norm
          return q, pval, res
  
      return q, pval
  
imports: from statsmodels.compat.pandas import deprecate_kwarg
  from collections.abc import Iterable
  import numpy
  import pandas
  from scipy import stats
  from statsmodels.regression.linear_model import OLS, RegressionResultsWrapper
  from statsmodels.stats._adnorm import anderson_statistic, normal_ad
  from statsmodels.stats._lilliefors import kstest_exponential, kstest_fit, kstest_normal, lilliefors
  from statsmodels.tools.validation import array_like, bool_like, dict_like, float_like, int_like, string_like
  from statsmodels.tsa.tsatools import lagmat
label_desc: Use image node_id 3 for calling a global function with example usage: compare_cox(results_x, results_z, store) and returns: q,  pval, q,  pval,  res