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# st: Incomplete results of linear regression with interaction variable

 From Jean-Baptiste Peraldi To statalist@hsphsun2.harvard.edu Subject st: Incomplete results of linear regression with interaction variable Date Wed, 20 Mar 2013 22:56:04 +0100

```Hi Statalisters,

I want to to run two linear regressions with dichotomous independant variables, where one contains an interaction variable.
It appears that the regression with the interaction variable gives only results for the coefficients.

Here is the content of my database:
***
. list
+---------------------------------------------------------------------------+
|         race   quality   mean_call    sd_call            n         r_q |
|----------------------------------------------------------------------------|
1. |           0        0        .0854185       .279624       1159         0 |
2. |           0        1        .1069024       .3091192     1188         0 |
3. | 	  1        0        .0569456       .2318388     1159         0 |
4. | 	  1        1        .0675791       .2511297     1169         1 |
+---------------------------------------------------------------------------+
***

The first regression is :
" mean_call = cst + beta1*race "
where "race" is a dichotomous (0 or 1) variable.

The second regression contains an interaction variable :
" mean_call = cst + beta1*race + beta2*quality + beta3*race*quality " where both "race" and "quality" are dichotomous (0 or 1) variables.

When running the first regression, I get full results:
***
. reg mean_call race

Source |      SS                    df       MS              	       Number of obs =       4
-------------+-----------------------------------------             F(  1,     2) =    8.00
Model      |  .001149076     1  .001149076           Prob > F      =  0.1056
Residual |  .000287314     2  .000143657           R-squared     =  0.8000
Total |   .00143639         3  .000478797           Root MSE      =  .01199

------------------------------------------------------------------------------
mean_call |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
race |   -.033898   .0119857    -2.83   0.106    -.0854683    .0176723
_cons |   .0961604   .0084752    11.35   0.008     .0596947    .1326261
------------------------------------------------------------------------------
***

For the second regression, I create the interaction variable and run the regression
***
. gen r_q = race*quality
. reg mean_call race quality r_q

Source |         SS                df       MS              	    Number of obs =       4
-------------+----------------------------------------           F(  3,     0) =       .
Model      |   .00143639     3  .000478797           Prob > F      =       .
Residual |       0                  0           .           	    R-squared     =  1.0000
Total |   .00143639     3  .000478797            Root MSE      =       0

------------------------------------------------------------------------------
mean_call |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
race |  -.0284728          .        .       .            .           .
quality |   .0214839          .        .       .            .           .
r_q |  -.0108504          .        .       .            .           .
_cons |   .0854185          .        .       .            .           .
------------------------------------------------------------------------------
***
Here we can see that we get results for the coefficients only, which is quite weird. I will be glad if you can help me solve this problem.

Jean-Baptiste P.

***
Stata/IC 12.1 for Mac (64-bit Intel)
Revision 25 Feb 2013
***

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```