title: F_Regression from sklearn.feature_selection
body: <p>I found the F_regression technique for feature selection in the sklearn feature selection module . I was not able to understand the principle it uses . 
  The description given was - <br><br>
  Univariate linear regression tests.<br>
  Quick linear model for testing the effect of a single regressor, sequentially for many regressors.
  This is done in 3 steps:</p>
  
  <ol>
  1.The regressor of interest and the data are orthogonalized wrt constant regressors.</ol>
  
  <ol>
  2. The cross correlation between data and regressors is computed.</ol>
  
  <ol>3. It is converted to an F score then to a p-value.</ol>
  
  <p>I am not able to understand this , please can someone explain this in layman terms. </p>