On 5 Feb 2002 18:01:15 -0800, [EMAIL PROTECTED] (Wuzzy) wrote: > > You made a model with the "exact same exposure in different units", > > which is something that no one would do, > > Hehe, translation is don't post messages until you've thought them > through. > > Anyway, turns out that the answer to my question is "No"..
Well, I think I speak for several statisticians when I say that we still don't know what you refer to as 'multi collinearity'. Do you mean 100%, as in your question? What *are* you asking? > Multicollinearity cannot force a correlation. It turns out that ONE > of the variables *was* correlated With R^2=0.45 and so > multicollinearity had no effect on overall R^2. [ ... ] If you are concluding that you won't improve R^2 by using exactly the same variable twice, you are correct. Another post-er has suggested where you *do* have to watch out for multi-colliinearity: He described the case where the multiple R^2 is large despite small univariate correlations with the criterion. (It was wordier than that, but that is what he did.) For further information-> You could search the last few months of posts in sci.stat.* using groups.google.com and look for 'confounding' or 'masking'; and there might be something more in my own stats-faq. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =================================================================
