title: Logit Regression in StatsModels Python
body: <p>I am attempting to run a logistic regression with one independent variable, fit the model to data and then return a probability output with a random out of sample input.</p>
  
  <pre><code>In [153]: df[['Diff1', 'Win']]
  Out[153]: 
     Diff1  Win
  0    100    1
  1    110    1
  2     20    0  
  3     80    1
  4    200    1
  5     25    0
  
  In [154]: logit = sm.Logit(df['Win'], df['Diff1'])
  
  In [155]: result=logit.fit()
  Optimization terminated successfully.
           Current function value: 0.451400
           Iterations 6
  
                              Logit Regression Results                           
  ==============================================================================
  Dep. Variable:                    Win   No. Observations:                    8
  Model:                          Logit   Df Residuals:                        7
  Method:                           MLE   Df Model:                            0
  Date:                Fri, 11 Dec 2015   Pseudo R-squ.:                  0.3177
  Time:                        13:49:07   Log-Likelihood:                -3.6112
  converged:                       True   LL-Null:                       -5.2925
                                          LLR p-value:                       nan
  ==============================================================================
                   coef    std err          z      P&gt;|z|      [95.0% Conf. Int.]
  ------------------------------------------------------------------------------
  Diff1          0.0207      0.014      1.435      0.151        -0.008     0.049
  ==============================================================================
  
  In [158]: result.predict(0)
  Out[158]: array([ 0.5])
  </code></pre>
  
  <p>I am clearly using the predict function incorrectly, because an input of 0 in this situation should not yield a 0.5.  This result is for a non-fitted example of a logistic model.  </p>
  
  <p>I would use simple OLS regression, but would like my model to be bounded by (0,1).  </p>