title: transforming "back" scaled coefficents after making a fit using standardScaler in conjunction with ols
body: <p>Im running a linear model using statsmodels.formula.api.ols after scaling the continuous input variables using sklearn.preprocessing.standardScaler</p>
  
  <p>So everything works ok during the model building. My issue is more of a technical one after the fitting is done and I want to convert the fitted coefficients back into the space where they can be used with un-scaled input. </p>
  
  <p>I know how standarScaler works: scaled =  (x - x_mean)/ sqrt(x_var). So for any given scaled x value, I can calculate the transformation back (by hand if I wanted to), (~ x = scaled*sqrt(x_var) + x_mean). </p>
  
  <p>What Im unsure of is how to apply this type of reverse scaling to the fit "scaled" linear coefficents. </p>
  
  <p>Can anyone explain how to do this?</p>
  
  <p>As a side note, Id really love to use sklearn.linear_model.LinearRegression with normalize=True in a pipeline... in this way the coefficents are scaled back for you (its much more elegant and streamlined)... alas, I really need the rich statistical output of the statsmodels ols method so that I can make an ANOVA analysis after the fit.</p>