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.

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

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 

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