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Re: st: Incomplete results of linear regression with interaction variable
From
William Buchanan <[email protected]>
To
[email protected]
Subject
Re: st: Incomplete results of linear regression with interaction variable
Date
Wed, 20 Mar 2013 15:14:16 -0700
Hi Jean-Baptiste,
Look at your F statistic. You don't have enough observations to estimate that model.
HTH,
Billy
On Mar 20, 2013, at 2:56 PM, Jean-Baptiste Peraldi <[email protected]> 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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