What Can I Do About Multicollinearity?

Solutions for Multicollinearity in Multiple Regression

 

What Can I Do About Multicollinearity?

 

     The best solution for dealing with multicollinearity is to understand the cause of multicollinearity and remove it. Multicollinearity occurs because two (or more) variables are related or they measure the same thing. If one of the variables in your model doesn’t seem essential to your model, removing it may reduce multicollinearity. Examining the correlations between variables and taking into account the importance of the variables will help you make a decision about what variables to drop from the model.
 

     You may also be able to find a way to combine the variables, but only if it seems logical. The impact of multicollinearity can be reduced by increasing the sample size of your study. You can also reduce multicollinearity by centering the variables. You can center variables by computing the mean of each independent variable, and then replacing each value with the difference between it and the mean. If this seems unclear to you, contact us for statistics consultation services.
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