def conf_int(self, alpha=0.05, coefs=None, return_df=False):
        """
        Creates the dataframe or array of lower and upper bounds for the
        (1-alpha)% confidence interval of the estimated parameters. Used when
        creating the statsmodels summary.

        Parameters
        ----------
        alpha : float, optional.
            Should be between 0.0 and 1.0. Determines the (1-alpha)% confidence
            interval that will be reported. Default == 0.05.
        coefs : array-like, optional.
            Should contain strings that denote the coefficient names that one
            wants the confidence intervals for. Default == None because that
            will return the confidence interval for all variables.
        return_df : bool, optional.
            Determines whether the returned value will be a dataframe or a
            numpy array. Default = False.

        Returns
        -------
        pandas dataframe or ndarray.
            Depends on return_df kwarg. The first column contains the lower
            bound to the confidence interval whereas the second column contains
            the upper values of the confidence intervals.
        """

        # Get the critical z-value for alpha / 2
        z_critical = scipy.stats.norm.ppf(1.0 - alpha / 2.0,
                                          loc=0, scale=1)

        # Calculate the lower and upper values for the confidence interval.
        lower = self.params - z_critical * self.standard_errors
        upper = self.params + z_critical * self.standard_errors
        # Name the series of lower / upper values for the confidence interval.
        lower.name = "lower"
        upper.name = "upper"

        # Combine the various series.
        combined = pd.concat((lower, upper), axis=1)

        # Subset the combined dataframe if need be.
        if coefs is not None:
            combined = combined.loc[coefs, :]

        # Return the desired object, whether dataframe or array
        if return_df:
            return combined
        else:
            return combined.values