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Fw: st: comparing coefficients across models

From   Dalhia <>
To   "" <>
Subject   Fw: st: comparing coefficients across models
Date   Sat, 4 Aug 2012 13:45:38 -0700 (PDT)


Thank you so much for helping me think this through. I very much appreciate it. 

Here are the sample sizes for group_dummy (0/1)

group dummy
            |      Freq.     Percent        Cum.
          0 |      6,298       81.85       81.85
          1 |      1,397       18.15      100.00
      Total |      7,695      100.00

Your assumptions are correct. Business group dummy is different from group_dummy. Also, the group_dummy in the interaction model is 0 when group==1 and 1 when group ==2. Also, I am only interested in whether the slope against x3 differs when group_dummy=0/1. I am not intersted in the intercept for the group dummy. From what I understand, I can get at the slope for x3 by running the regression for the two groups separately, and then comparing the coefficients for x3. Is this correct? Or are you saying something different?

As you suggested I also looked at the graphs for the relationship between the two variables that are highly correlated (degree centrality and betweenness centrality) for (1) the whole sample, (2) for group_dummy==0 and (3) for group_dummy==1. The three graphs look extremely similar with a negative, slightly curving slope (I can't seem to figure out how to attach a file, and not get my mail bounced from statalist). Also, I apologize, but I made a mistake in my earlier email, the high correlation is between x1 degree centrality and x3 betweenness centrality, and not between x1 and x2. 

Thanks once again for your help

----- Original Message -----
From: David Hoaglin <>
Sent: Friday, August 3, 2012 8:48 PM
Subject: Re: st: comparing coefficients across models


Thanks for the correction.

I may not understand the relation between the variable group, which
took the values 1 and 2 in your initial example, and group_dummy in
the interaction model.  I assume that group_dummy is 0 when group = 1
and 1 when group = 2.

I'm also assuming that group_dummy is different from x2.

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