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,
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