How to interpret correlation matrix? I have built a correlation matrix to check multicollinearity in a regression model. But how to interpret this?


Can we say that there is a certain correlation value from which the independent variables related to this correlation should be removed from the model? Is this the value over 0.5?
Are there any better solutions than removing the conflicting variables (when correlation value is too big)? I tried normalization but it didn't help.
Regarding 1) Usually you would check this based on &quot;tests&quot; like the VIF (see this post for details). I personally would not be too worried with a correlation of 0.5.
Regarding 2) Not really since you would need to &quot;decorrelate&quot; the variables and you can only do linear transformations (when you want to preserve the variable(s)). What you can do is to make projections, e.g. by principle components, but this will totally change the variable set.