![]() ![]() In regression, we are trying to build a model to predict Y based on certain predictor variables (x 1, x 2, etc.). Please feel free to leave a comment at the end of this page. You may download a pdf version of this publication at this link. ![]() So, we will take a look at how stepwise regression can easily build a model for you as well as a few of the drawbacks of stepwise regression. As with all techniques, there are some caveats about using stepwise regression. ![]() Sit back and let the model be built for you. On the surface, this technique sounds great. This month’s publication takes a look at stepwise regression. This is an automated process that builds a regression model for you by going through a series of steps of adding the most significant variable or removing the least significant variable. ![]() This is where stepwise regression can help. How can you go through these 40 variables to see which ones really impact sales and could become part of a model to predict sales? Well, you could run a full regression analysis with all 40 variables and see which ones are “significant.” But regression models change as variables are added or removed. You have a database that contains 40 different variables that might impact sales. You would like to be able to predict what sales will be. ![]()
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