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: Confidence interval construction for the predicted mean.
  
  This is currently only available for t and z tests.
  
  Parameters
  ----------
  alpha : float, optional
      The significance level for the prediction interval.
      The default `alpha` = .05 returns a 95% confidence interval.
  
  Returns
  -------
  pi : {ndarray, DataFrame}
      The array has the lower and the upper limit of the prediction
      interval in the columns.
docstr_tok: ["Confidence","interval","construction","for","the","predicted","mean",".","This","is","currently","only","available","for","t","and","z","tests",".","Parameters","--","--","--","--","--","alpha",":","float",",","optional","The","significance","level","for","the","prediction","interval",".","The","default","`","alpha","`","=",".05","returns","a","95","%","confidence","interval",".","Returns","--","--","--","-","pi",":","{","ndarray",",","DataFrame","}","The","array","has","the","lower","and","the","upper","limit","of","the","prediction","interval","in","the","columns","."]
code: def conf_int(self, alpha=0.05):
      """
      Confidence interval construction for the predicted mean.
  
      This is currently only available for t and z tests.
  
      Parameters
      ----------
      alpha : float, optional
          The significance level for the prediction interval.
          The default `alpha` = .05 returns a 95% confidence interval.
  
      Returns
      -------
      pi : {ndarray, DataFrame}
          The array has the lower and the upper limit of the prediction
          interval in the columns.
      """
      se = self.se_mean
      q = self.dist.ppf(1 - alpha / 2.0, *self.dist_args)
      lower = self.predicted_mean - q * se
      upper = self.predicted_mean + q * se
      ci = np.column_stack((lower, upper))
      if self._use_pandas:
          return self._wrap_pandas(ci, columns=["lower", "upper"])
      return ci
  
file_path: statsmodels/statsmodels/tsa/base/prediction.py
imports: import numpy
  import pandas
  from scipy import stats
label_desc: Use image node_id 9 for calling the PredictionResults obj's underlying member method code with example usage: obj.conf_int(alpha) and returns: ci, self