class_docstr: Results class for predictions.
  
  Parameters
  ----------
  predicted_mean : ndarray
      The array containing the prediction means.
  var_pred_mean : ndarray
      The array of the variance of the prediction means.
  var_resid : ndarray
      The array of residual variances.
  df : int
      The degree of freedom used if dist is 't'.
  dist : {'norm', 't', object}
      Either a string for the normal or t distribution or another object
      that exposes a `ppf` method.
  row_labels : list[str]
      Row labels used in summary frame.
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docstr: Returns the confidence interval of the value, `effect` of the
  constraint.
  
  This is currently only available for t and z tests.
  
  Parameters
  ----------
  alpha : float, optional
      The significance level for the confidence interval.
      ie., The default `alpha` = .05 returns a 95% confidence interval.
  
  Returns
  -------
  ci : ndarray, (k_constraints, 2)
      The array has the lower and the upper limit of the confidence
      interval in the columns.
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code: def conf_int(self, obs=False, alpha=0.05):
      """
      Returns the confidence interval of the value, `effect` of the
      constraint.
  
      This is currently only available for t and z tests.
  
      Parameters
      ----------
      alpha : float, optional
          The significance level for the confidence interval.
          ie., The default `alpha` = .05 returns a 95% confidence interval.
  
      Returns
      -------
      ci : ndarray, (k_constraints, 2)
          The array has the lower and the upper limit of the confidence
          interval in the columns.
      """
  
      se = self.se_obs if obs else 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
      return np.column_stack((lower, upper))
  
file_path: statsmodels/statsmodels/regression/_prediction.py
imports: import numpy
  from scipy import stats
  import pandas
label_desc: Use image node_id 7 for calling the PredictionResults obj's underlying member method code with example usage: obj.conf_int(obs, alpha) and returns: np