title: confidence and prediction intervals with StatsModels
body: <p>I do this <code>linear regression</code> with <code>StatsModels</code>:</p>
  
  <pre><code>import numpy as np
  import statsmodels.api as sm
  from statsmodels.sandbox.regression.predstd import wls_prediction_std
  
  n = 100
  
  x = np.linspace(0, 10, n)
  e = np.random.normal(size=n)
  y = 1 + 0.5*x + 2*e
  X = sm.add_constant(x)
  
  re = sm.OLS(y, X).fit()
  print(re.summary())
  
  prstd, iv_l, iv_u = wls_prediction_std(re)
  </code></pre>
  
  <p>My questions are, <code>iv_l</code> and <code>iv_u</code> are the upper and lower <em>confidence intervals</em> or <em>prediction intervals</em>? </p>
  
  <p>How I get others? </p>
  
  <p>I need the confidence and prediction intervals for all points, to do a plot.</p>