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st: RE: regression with categorical predictors
From
"Feiveson, Alan H. (JSC-SK311)" <[email protected]>
To
"[email protected]" <[email protected]>
Subject
st: RE: regression with categorical predictors
Date
Tue, 30 Aug 2011 11:57:22 -0500
Amir - You are correct. There is no automatic adjustment.
al Feiveson
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of amir gahremanpour
Sent: Tuesday, August 30, 2011 11:07 AM
To: statalist
Subject: st: regression with categorical predictors
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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