Any help will be appreciated greatly. I have one dependent y, and 4 indepent varaibles x1 x2 x3 x4 are correlated, and 3 indep z1 z2 z3 are correlated, 4 indep w1 w2 w3 w4 are also correlated. I would like to regress y on all 11 predictors to build one model, and include them as many as possible.

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11/6/2009 9:53:49 PM

On Nov 6, 4:53=A0pm, Frank <danbomingzhi1...@sina.com> wrote: > Any help will be appreciated greatly. I have one dependent y, and 4 > indepent varaibles x1 x2 x3 x4 are correlated, and 3 indep z1 z2 z3 > are correlated, 4 indep w1 w2 w3 w4 are also correlated. > I would like to regress y on all 11 predictors to build one model, and > include them as many as possible. Run collinearity diagnostics and don't forget to look for plausible effect modifiers. In the end, you may have to make this decision on a clinical basis, not a statistical basis. You could also use factor analysis or other methods. No matter what you do, limitations and compromises are coming. Marc

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11/7/2009 2:42:02 PM

On Sat, 7 Nov 2009 06:42:02 -0800 (PST), mcap <mcam54@yahoo.com> wrote: >On Nov 6, 4:53�pm, Frank <danbomingzhi1...@sina.com> wrote: >> Any help will be appreciated greatly. I have one dependent y, and 4 >> indepent varaibles x1 x2 x3 x4 are correlated, and 3 indep z1 z2 z3 >> are correlated, 4 indep w1 w2 w3 w4 are also correlated. >> I would like to regress y on all 11 predictors to build one model, and >> include them as many as possible. > >Run collinearity diagnostics and don't forget to look for plausible >effect modifiers. In the end, you may have to make this decision on a >clinical basis, not a statistical basis. You could also use factor >analysis or other methods. No matter what you do, limitations and >compromises are coming. > >Marc Frank wants to use as many predictors as possible. Marc warns of compromises. Marc could be jumping the gun. Frank apparently expects a problem, but he did not establish that that is any *real* problem. What happens when the equation uses them all? Diagnositics? And then? What N is available to work with, to establish what sort of R^2? If the W's, X's and Z's each represent a sensible "latent variable", and those latent variables are expected to carry the prediction, then there is also no problem -- The approach *should* be shortened to one that creates the 3 composite variables and then uses those three for the prediction. -- Rich Ulrich

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11/7/2009 11:55:29 PM

Many thanks to Marc and Rich for your great help! Frank

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11/8/2009 2:47:50 AM