title: Is there a Python Statsmodels OLS function to get only the statistical significant coefficients after fit a regression?
body: <p>I have fit a linear regression using the OLS.fit() function of Statsmodels, obtained the estimated coefficients and corresponding p-values by calling the .params and .pvalues of the results classes. </p>
  
  <p>Among the obtained parameters, few of them are statistically significant (i.e. at 5% significance level), is there any function can return the significant parameters only?</p>
  
  <p>Have tried calling the .params after fitting the OLS, but this returns ALL coefficients.</p>
  
  <pre><code>Y = [1,3,4,5,2,3,4,20,100]
  X1 = range(1,10)
  X1 = sm.add_constant(X1)
  results = sm.OLS(Y,X1).fit()
  print(results.summary())
  results.params
  results.pvalues
  </code></pre>
  
  <p>Actual results:</p>
  
  <pre><code>results.params returned:
  array([-21.30555556,   7.41666667])
  
  results.pvalues returned:
  array([0.30606981, 0.06733325])
  </code></pre>
  
  <p>Expected results:</p>
  
  <p>Only return the params 7.41666667, since the corresponding p-value is significant at 10%, 0.06733325 is smaller than 10%</p>