title: Building multi-regression model throws error: `Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).`
body: <p>I have pandas dataframe with some categorical predictors (i.e. variables) as 0 &amp; 1, and some numeric variables. When I fit that to a stasmodel like:</p>
  
  <pre><code>est = sm.OLS(y, X).fit()
  </code></pre>
  
  <p>It throws:</p>
  
  <pre><code>Pandas data cast to numpy dtype of object. Check input data with np.asarray(data). 
  </code></pre>
  
  <p>I converted all the dtypes of the DataFrame using <code>df.convert_objects(convert_numeric=True)</code></p>
  
  <p>After this all dtypes of dataframe variables appear as int32 or int64. But at the end it still shows <code>dtype: object</code>, like this:</p>
  
  <pre><code>4516        int32
  4523        int32
  4525        int32
  4531        int32
  4533        int32
  4542        int32
  4562        int32
  sex         int64
  race        int64
  dispstd     int64
  age_days    int64
  dtype: object
  </code></pre>
  
  <p>Here 4516, 4523 are variable labels.</p>
  
  <p>Any idea? I need to build a multi-regression model on more than hundreds of variables. For that I have concatenated 3 pandas DataFrames to come up with final DataFrame to be used in model building. </p>