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st: regression with categorical predictors
From 
 
amir gahremanpour <[email protected]> 
To 
 
statalist <[email protected]> 
Subject 
 
st: regression with categorical predictors 
Date 
 
Tue, 30 Aug 2011 11:06:52 -0500 
Hello everybody,
First let me apologize if my question is very primitive, I am trying to self educate myself about regression with stata. 
When I have a categorical predictor with more than two levels , I am not sure if the p-values stata provides are adjusted for multiple comparison or are not? This example is from stata documentation , variable region has 4 levels , are the p-values adjusted for 3 comparisons to the base level? I do not think so !
thanks a lot
 
 use http://www.stata-press.com/data/r12/census9
(1980 Census data by state)
. regress drate medage i.region [w=pop]
(analytic weights assumed)
(sum of wgt is   2.2591e+08)
      Source |       SS       df       MS              Number of obs =      50
-------------+------------------------------           F(  4,    45) =   37.21
       Model |   4096.6093     4  1024.15232           Prob > F      =  0.0000
    Residual |  1238.40987    45  27.5202192           R-squared     =  0.7679
-------------+------------------------------           Adj R-squared =  0.7472
       Total |  5335.01916    49  108.877942           Root MSE      =   5.246
------------------------------------------------------------------------------
       drate |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      medage |   4.283183   .5393329     7.94   0.000     3.196911    5.369455
             |
      region |
          2  |   .3138738   2.456431     0.13   0.899    -4.633632     5.26138
          3  |  -1.438452   2.320244    -0.62   0.538    -6.111663    3.234758
          4  |  -10.90629   2.681349    -4.07   0.000    -16.30681   -5.505777
             |
       _cons |  -39.14727   17.23613    -2.27   0.028    -73.86262   -4.431915
------------------------------------------------------------------------------
. * testing the joint significance of joint variable
 		 	   		  
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