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Date:         Wed, 2 May 2007 08:59:56 -0400
Reply-To:     Raghu Venkat <raghustays@GMAIL.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Raghu Venkat <raghustays@GMAIL.COM>
Subject:      Re: Which SAS procedure to use
Comments: To:
In-Reply-To:  <>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

try to first see whats in the data. a correlation matrix using proc corr might be a good starting point to see if any multicollinearity issues exist. Then see if you can combine some of those variables and reduce it some other variable that might make sense.

i dont think anyone here will have a ready made solution to your question. This sounds like one of the class projects :)

- Raghu

On 5/2/07, Kunal Kelkar <> wrote: > > Hi All, > > I am having a SAS dataset with close to 640 > variables out of which 430 are of categorical type.I am trying to fit > regression model using this data. My response variable is also of > categorical type.Many variables are having lot of missing values.I need > help to find out how I can reduce the number of variables and how can tackle > the null values.Also which SAS procedure will be helpful for this. If > possible if anyone can provide some example that will be great. > > Thanks in advance. > > Thanks, > Kunal > > ________________________________________________________________________ > Check Out the new free AIM(R) Mail -- 2 GB of storage and industry-leading > spam and email virus protection. >

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