![]() You just pop the variables into the model as they occur to you or just because the data are readily available. Perhaps there is an actual relationship? Or is it just a chance correlation? Some of the independent variables will be statistically significant. Every time you add a variable, the R-squared increases, which tempts you to add more. It’s an incredibly tempting statistical analysis that practically begs you to include additional independent variables in your model. Multiple linear regression can seduce you! Yep, you read it here first. Does this graph display an actual relationship or is it an overfit model? This blog post shows you how to make this determination. These statistics help you include the correct number of independent variables in your regression model. The protection that adjusted R-squared and predicted R-squared provide is critical because too many terms in a model can produce results that you can’t trust. Adjusted R-squared and predicted R-squared use different approaches to help you fight that impulse to add too many. R-squared tends to reward you for including too many independent variables in a regression model, and it doesn’t provide any incentive to stop adding more.
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