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st: regression using dummy variables


From   Amy Jennings <faith79@hotmail.co.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: regression using dummy variables
Date   Thu, 3 Sep 2009 12:47:35 +0100

 
Hi,
 
I am new to STATA and am currently doing (trying) multiple regression using dummy coded categorical variables, I want to compare my coefficients so I can assess the relative strength of each predictor as I would in multiple regression with continuous predictors.  is it possible to get an overall coefficient for my dummy coded variables? 
 
 
. regress  Group27_weight  highested_22 highested_23 highested_24 BMI_M2 BMI_M3, beta
      Source |       SS       df       MS              Number of obs =    1602
-------------+------------------------------           F(  5,  1596) =    8.74
       Model |  512485.705     5  102497.141           Prob> F      =  0.0000
    Residual |  18722950.7  1596  11731.1721           R-squared     =  0.0266
-------------+------------------------------           Adj R-squared =  0.0236
       Total |  19235436.4  1601  12014.6386           Root MSE      =  108.31
------------------------------------------------------------------------------
Group27_we~t |      Coef.   Std. Err.      t    P>|t|                     Beta
-------------+----------------------------------------------------------------
highested_22 |   24.77708   11.51337     2.15   0.032                 .1130292
highested_23 |   23.56448   12.14225     1.94   0.052                 .0935415
highested_24 |   62.28482   12.72192     4.90   0.000                 .2149551
      BMI_M2 |  -9.841873   6.368431    -1.55   0.122                -.0395633
      BMI_M3 |  -16.02685   7.922844    -2.02   0.043                -.0518379
       _cons |   149.9236   11.31615    13.25   0.000                        .
------------------------------------------------------------------------------

 
e.g. is it possible to assess if highested_2 or BMI_M has the largest coefficient? or standardised coefficent? 
 
Many thanks in advance, 
 
kind regards 
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