title: Capturing high multi-collinearity in statsmodels
body: <p>Say I fit a model in statsmodels</p>
  
  <pre><code>mod = smf.ols('dependent ~ first_category + second_category + other', data=df).fit()
  </code></pre>
  
  <p>When I do <code>mod.summary()</code> I may see the following:</p>
  
  <pre><code>Warnings:
  [1] The condition number is large, 1.59e+05. This might indicate that there are
  strong multicollinearity or other numerical problems.
  </code></pre>
  
  <p>Sometimes the warning is different (e.g. based on eigenvalues of the design matrix). How can I capture <strong>high-multi-collinearity</strong> conditions in a variable? Is this warning stored somewhere in the model object?</p>
  
  <p>Also, where can I find a description of the fields in <code>summary()</code>? </p>