class_docstr: Minimal implementation of WLS optimized for performance.
  
  Parameters
  ----------
  endog : array_like
      1-d endogenous response variable. The dependent variable.
  exog : array_like
      A nobs x k array where `nobs` is the number of observations and `k`
      is the number of regressors. An intercept is not included by default
      and should be added by the user. See
      :func:`statsmodels.tools.add_constant`.
  weights : array_like, optional
      1d array of weights.  If you supply 1/W then the variables are pre-
      multiplied by 1/sqrt(W).  If no weights are supplied the default value
      is 1 and WLS reults are the same as OLS.
  check_endog : bool, optional
      Flag indicating whether to check for inf/nan in endog.
      If True and any are found, ValueError is raised.
  check_weights : bool, optional
      Flag indicating whether to check for inf/nan in weights.
      If True and any are found, ValueError is raised.
  
  Notes
  -----
  Provides only resid, scale, fittedvalues, model.weights which are used by
  methods that iteratively apply WLS.
  
  Does not perform any checks on the input data for type or shape
  compatibility
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docstr: Construct results
  
  params : ndarray
      Model parameters
  
  Notes
  -----
  Allows results to be constructed from either existing parameters or
  when estimated using using ``fit``
docstr_tok: ["Construct","results","params",":","ndarray","Model","parameters","Notes","--","--","-","Allows","results","to","be","constructed","from","either","existing","parameters","or","when","estimated","using","using","``","fit","``"]
code: def results(self, params):
      """
      Construct results
  
      params : ndarray
          Model parameters
  
      Notes
      -----
      Allows results to be constructed from either existing parameters or
      when estimated using using ``fit``
      """
      fitted_values = self.exog.dot(params)
      resid = self.endog - fitted_values
      wresid = self.wendog - self.wexog.dot(params)
      df_resid = self.wexog.shape[0] - self.wexog.shape[1]
      scale = np.dot(wresid, wresid) / df_resid
  
      return Bunch(
          params=params,
          fittedvalues=fitted_values,
          resid=resid,
          model=self,
          scale=scale,
      )
  
imports: import numpy
  from statsmodels.tools.tools import Bunch
label_desc: Use image node_id 3 for calling the _MinimalWLS obj's underlying member method code with example usage: obj.results(params) and returns: Bunch