code: def _conf_set(F, alpha=0.05):
      r"""
      Constructs a Dvoretzky-Kiefer-Wolfowitz confidence band for the eCDF.
  
      Parameters
      ----------
      F : array_like
          The empirical distributions
      alpha : float
          Set alpha for a (1 - alpha) % confidence band.
  
      Notes
      -----
      Based on the DKW inequality.
  
      .. math:: P \left( \sup_x \left| F(x) - \hat(F)_n(X) \right| >
         \epsilon \right) \leq 2e^{-2n\epsilon^2}
  
      References
      ----------
      Wasserman, L. 2006. `All of Nonparametric Statistics`. Springer.
      """
      nobs = len(F)
      epsilon = np.sqrt(np.log(2.0 / alpha) / (2 * nobs))
      lower = np.clip(F - epsilon, 0, 1)
      upper = np.clip(F + epsilon, 0, 1)
      return lower, upper
  
file_path: statsmodels/statsmodels/distributions/empirical_distribution.py
imports: import numpy
  from scipy.interpolate import interp1d
label_desc: Use image node_id 1 for calling a global function with example usage: _conf_set(F, alpha) and returns: lower,  upper