class_docstr: Prediction results
  
  Parameters
  ----------
  predicted_mean : {ndarray, Series, DataFrame}
      The predicted mean values
  var_pred_mean : {ndarray, Series, DataFrame}
      The variance of the predicted mean values
  dist : {None, "norm", "t", rv_frozen}
      The distribution to use when constructing prediction intervals.
      Default is normal.
  df : int, optional
      The degree of freedom parameter for the t. Not used if dist is None,
      "norm" or a callable.
  row_labels : {Sequence[Hashable], pd.Index}
      Row labels to use for the summary frame. If None, attempts to read the
      index of ``predicted_mean``
docstr: Summary frame of mean, variance and confidence interval.
  
  Returns
  -------
  DataFrame
      DataFrame containing four columns:
  
      * mean
      * mean_se
      * mean_ci_lower
      * mean_ci_upper
  
  Notes
  -----
  Fixes alpha to 0.05 so that the confidence interval should have 95%
  coverage.
docstr_tok: ["Summary","frame","of","mean",",","variance","and","confidence","interval",".","Returns","--","--","--","-","DataFrame","DataFrame","containing","four","columns",":","*","mean","*","mean_se","*","mean_ci_lower","*","mean_ci_upper","Notes","--","--","-","Fixes","alpha","to","0.05","so","that","the","confidence","interval","should","have","95","%","coverage","."]
code: def summary_frame(self, alpha=0.05):
      """
      Summary frame of mean, variance and confidence interval.
  
      Returns
      -------
      DataFrame
          DataFrame containing four columns:
  
          * mean
          * mean_se
          * mean_ci_lower
          * mean_ci_upper
  
      Notes
      -----
      Fixes alpha to 0.05 so that the confidence interval should have 95%
      coverage.
      """
      ci_mean = np.asarray(self.conf_int(alpha=alpha))
      lower, upper = ci_mean[:, 0], ci_mean[:, 1]
      to_include = {
          "mean": self.predicted_mean,
          "mean_se": self.se_mean,
          "mean_ci_lower": lower,
          "mean_ci_upper": upper,
      }
      return pd.DataFrame(to_include)
  
file_path: statsmodels/statsmodels/tsa/base/prediction.py
imports: import numpy
  import pandas
  from scipy import stats
label_desc: Use image node_id 10 for calling the PredictionResults obj's underlying member method code with example usage: obj.summary_frame(alpha) and returns: pd