|Date: ||Mon, 30 Jun 1997 04:49:12 GMT|
|Reply-To: ||DNordlund <dnordlund@AOL.COM>|
|Sender: ||"SAS(r) Discussion" <SAS-L@UGA.CC.UGA.EDU>|
|From: ||DNordlund <dnordlund@AOL.COM>|
|Organization: ||AOL http://www.aol.com|
|Subject: ||Re: Singular Matrix Problem|
In article <5p12id$12pa@ns5-1.CC.Lehigh.EDU>, drt2@Lehigh.EDU writes:
>Subject: Singular Matrix Problem
>Date: 27 Jun 1997 14:59:25 -0400
>Sorry in advance if this is a "statistics" question vs. a SAS question
>am desperate for some help.
>I am doing an analysis using several continuous and discrete variables. I
>using Proc GLM with the /solution option to get the beta estimates. I get
>note that says" The X'X matrix has been found to be singular and a
>inverse was used to solve the normal equations. Estimates ... are biased
>are not unique estimates of the parameters."
>I understand what it is saying but, and here's the SAS question: What can
>to "fix" this problem? I did a correlation analysis but there does not
>to be any significant correlation between the variables in the model.
>anyone help me with this?
>While I'm abusing this group, can anyone tell me the "best" way to
>the Beta Coefficients? Is it better to calculate them with all variables
>the model or alone?
>Thanks in advance.
the fix for your problem is hard to specify without knowing the nature of
your predictor variables. Doing simple pairwise correlations between
variables will not necessarily point you toward the problem. It is often
the case that some one variable is a linear combination of two or more
other variables. In this case, none of the paiwise correlations are
likely to be high. This often happens when dummy coding categorical
variables and you forget to "leave out" the comparison level.
If you describe your variables in a little more detail someone may be able
to provide a little more guidance. This would also help with answering
your second question.