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Re: st: Incomplete results of linear regression with interaction variable


From   John Antonakis <John.Antonakis@unil.ch>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Incomplete results of linear regression with interaction variable
Date   Thu, 21 Mar 2013 08:21:15 +0100

Hi.

You dont have enough Dfs. See how DFs are calculated for an F-test.

You have k=3 parameters in the numerator and n-k-1 (0) in the denominator.

Did you really mean to estimate a model with n= 4?

Best
J.

__________________________________________

John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

Associate Editor
The Leadership Quarterly
__________________________________________

On 20.03.2013 22:56, Jean-Baptiste Peraldi wrote:
> 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
> -------------+----------------------------------------- Adj R-squared = 0.7000 > 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 > -------------+---------------------------------------- Adj R-squared = . > 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.
> Thanks for your consideration.
>
> Jean-Baptiste P.
>
> ***
> Stata/IC 12.1 for Mac (64-bit Intel)
> Revision 25 Feb 2013
> ***
>
>
>
>
>
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