I'm a little confused. So, multicollinearity is a problem that can affect our regression results when the independent variables are correlated with each other. But many times, I see regression models like this: y = B0 + B1 *Factor1 + B2 * (Factor1)^squared

So, wouldn't Factor 1 and (Factor 1)^squared be highly correlated, thus resulting in a big collinearity problem? Any ideas why its ok here? Thanks. -- View this message in context: http://www.nabble.com/Multicollinearity-confusion-tp18538040p18538040.html Sent from the SPSSX Discussion mailing list archive at Nabble.com.

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