# Operational research and Linear regression

I have a pretty large data set with several independent variables and one variable that I would like to explain the behavior (dependent variable).

What I want to do is find the linear coefficient and the independent variables that would minimize the error factor.

In your opinion can I use an OR algorithm to setup the search of variables and coefficient until error is minimal? Have you ever tried this ?

• It looks like you need a classical multiple linear regression ? Such a regression will give you the optimal coefficients that minimize the standard error (or the squared error). Feb 3 '21 at 8:32
• My question is instead of doing try / error method can I use operational research algorithms to get optimal equation Feb 3 '21 at 9:02
• If I understand correctly, you need a multiple linear regression. This is typically solved with an optimization algorithm (and not trial error). Any linear regression tool (such as the one in Excel) will give you the optimal coefficients. Feb 3 '21 at 9:16
• Excel will give you the optimal coefficient for each variable.. he will not select the variables for you. I’m Feb 3 '21 at 9:20
• You should probably include a summary of this paper in your question, and specify that you want an algorithm that also removes "useless" variables. But the answer to your initial question is in the link you provided : YES, OR can be used for this, and there is probably no better way. Excel does not remove variables, but gives you the information for you to do it yourself. And tools like RapidMiner do it automatically. Probably most modern tools do it as well. Feb 3 '21 at 9:57