title: Preserve variable names in summary from statsmodels
body: <p>I am using OLS from statsmodel, the link is <a href="https://www.statsmodels.org/stable/examples/notebooks/generated/ols.html" rel="noreferrer">https://www.statsmodels.org/stable/examples/notebooks/generated/ols.html</a></p>
  
  <pre><code>#USD
  X = sm.add_constant(USD)
  model = sm.OLS(y, X)
  results = model.fit()
  print(results.summary())
                                   OLS Regression Results                                 
  ========================================================================================
  Dep. Variable:     All Ordinaries closing price   R-squared:                       0.265
  Model:                                      OLS   Adj. R-squared:                  0.265
  Method:                           Least Squares   F-statistic:                     352.4
  Date:                          Tue, 23 Oct 2018   Prob (F-statistic):           2.35e-67
  Time:                                  17:30:24   Log-Likelihood:                -8018.8
  No. Observations:                           977   AIC:                         1.604e+04
  Df Residuals:                               975   BIC:                         1.605e+04
  Df Model:                                     1                                         
  Covariance Type:                      nonrobust                                         
  ==============================================================================
                   coef    std err          t      P&gt;|t|      [0.025      0.975]
  ------------------------------------------------------------------------------
  const       1843.1414    149.675     12.314      0.000    1549.418    2136.864
  USD         3512.5040    187.111     18.772      0.000    3145.318    3879.690
  ==============================================================================
  Omnibus:                      276.458   Durbin-Watson:                   0.009
  Prob(Omnibus):                  0.000   Jarque-Bera (JB):               74.633
  Skew:                           0.438   Prob(JB):                     6.22e-17
  Kurtosis:                       1.967   Cond. No.                         10.7
  ==============================================================================
  
  Warnings:
  [1] Standard Errors assume that the covariance matrix of the errors is correctly specified
  </code></pre>
  
  <p>You can see the X is showing as USD in the summary which is what I want.
  However, after adding a new variable</p>
  
  <pre><code>#JPY + USD
  X = sm.add_constant(JPY)
  X = np.column_stack((X, USD))
  model = sm.OLS(y, X)
  results = model.fit()
  print(results.summary())
  
  
   OLS Regression Results                                 
  ========================================================================================
  Dep. Variable:     All Ordinaries closing price   R-squared:                       0.641
  Model:                                      OLS   Adj. R-squared:                  0.640
  Method:                           Least Squares   F-statistic:                     868.8
  Date:                          Tue, 23 Oct 2018   Prob (F-statistic):          2.80e-217
  Time:                                  17:39:19   Log-Likelihood:                -7669.4
  No. Observations:                           977   AIC:                         1.534e+04
  Df Residuals:                               974   BIC:                         1.536e+04
  Df Model:                                     2                                         
  Covariance Type:                      nonrobust                                         
  ==============================================================================
                   coef    std err          t      P&gt;|t|      [0.025      0.975]
  ------------------------------------------------------------------------------
  const      -1559.5880    149.478    -10.434      0.000   -1852.923   -1266.253
  x1            78.6589      2.466     31.902      0.000      73.820      83.497
  x2          -366.5850    178.672     -2.052      0.040    -717.211     -15.958
  ==============================================================================
  Omnibus:                       24.957   Durbin-Watson:                   0.031
  Prob(Omnibus):                  0.000   Jarque-Bera (JB):               27.278
  Skew:                           0.353   Prob(JB):                     1.19e-06
  Kurtosis:                       3.415   Cond. No.                         743.
  ==============================================================================
  
  Warnings:
  [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
  </code></pre>
  
  <p>It is not showing USD and JPY, but x1 x2. Is there a way to fix it? I tried google but found nothing. </p>