code: def cov_white_simple(results, use_correction=True):
      """
      heteroscedasticity robust covariance matrix (White)
  
      Parameters
      ----------
      results : result instance
         result of a regression, uses results.model.exog and results.resid
         TODO: this should use wexog instead
  
      Returns
      -------
      cov : ndarray, (k_vars, k_vars)
          heteroscedasticity robust covariance matrix for parameter estimates
  
      Notes
      -----
      This produces the same result as cov_hc0, and does not include any small
      sample correction.
  
      verified (against LinearRegressionResults and Peterson)
  
      See Also
      --------
      cov_hc1, cov_hc2, cov_hc3 : heteroscedasticity robust covariance matrices
          with small sample corrections
  
      """
      xu, hessian_inv = _get_sandwich_arrays(results)
      sigma = S_white_simple(xu)
  
      cov_w = _HCCM2(hessian_inv, sigma)  # add bread to sandwich
  
      if use_correction:
          nobs, k_params = xu.shape
          cov_w *= nobs / float(nobs - k_params)
  
      return cov_w
  
imports: import numpy
  from statsmodels.tools.grouputils import combine_indices, group_sums
  from statsmodels.stats.moment_helpers import se_cov
label_desc: Use image node_id 18 for calling a global function with example usage: cov_white_simple(results, use_correction) and returns: cov_w