We quickly become very accustomed to reading and interpreting regression analyses from our computer output. Regression analyses as reported in journals and other publications often look very different. Here are the results of a regression a typical analysis as produced by Stata.
use http://www.philender.com/courses/data/hsbdemo, clear
regress write female read socst i.prog
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 5, 194) = 42.68
Model | 9364.97492 5 1872.99498 Prob > F = 0.0000
Residual | 8513.90008 194 43.8860829 R-squared = 0.5238
-------------+------------------------------ Adj R-squared = 0.5115
Total | 17878.875 199 89.843593 Root MSE = 6.6247
------------------------------------------------------------------------------
write | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | 4.916003 .9478479 5.19 0.000 3.046593 6.785412
read | .3427226 .060063 5.71 0.000 .2242624 .4611829
socst | .2685807 .0584637 4.59 0.000 .1532746 .3838868
|
prog |
2 | .9975073 1.233344 0.81 0.420 -1.434978 3.429992
3 | -1.888857 1.389299 -1.36 0.176 -4.628927 .8512127
|
_cons | 18.06893 3.068803 5.89 0.000 12.01643 24.12143
------------------------------------------------------------------------------
Publication tables usually have only the regression coefficient and standard errors along
with some fit statistics. In Stata, the estimates table command can be used to create
publication type tables. Unfortunately, the star option is not
allowed with the standard errors. Below are two examples:
estimates store m1
estimates table m1, stats(N r2 r2_a) b(%6.3f) star
---------------------------
Variable | m1
-------------+-------------
female | 4.916***
read | 0.343***
socst | 0.269***
|
prog |
2 | 0.998
3 | -1.889
|
_cons | 18.069***
-------------+-------------
N | 200
r2 | 0.524
r2_a | 0.512
---------------------------
legend: * p<0.05; ** p<0.01; *** p<0.001
estimates table m1, stats(N r2 r2_a) se b(%6.3f) se(%6.3f)
------------------------
Variable | m1
-------------+----------
female | 4.916
| 0.948
read | 0.343
| 0.060
socst | 0.269
| 0.058
|
prog |
2 | 0.998
| 1.233
3 | -1.889
| 1.389
|
_cons | 18.069
| 3.069
-------------+----------
N | 200
r2 | 0.524
r2_a | 0.512
------------------------
legend: b/se
Alternatively, you can use the outreg2 command (findit outreg2) to produce an ASCII
table of the results. Note: You will usually need to add spaces manually to get the columns to
line up correctly.
outreg2, see
VARIABLES write Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
female 4.916***
(0.948)
read 0.343***
(0.0601)
socst 0.269***
(0.0585)
2.prog 0.998
(1.233)
3.prog -1.889
(1.389)
Constant 18.07***
(3.069)
Observations 200
R-squared 0.524
Now, let's run a second regression model with interaction and display the results of both
analyses in the same table.
regress write female read i.prog##c.socst
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 7, 192) = 31.84
Model | 9604.82727 7 1372.11818 Prob > F = 0.0000
Residual | 8274.04773 192 43.0939986 R-squared = 0.5372
-------------+------------------------------ Adj R-squared = 0.5203
Total | 17878.875 199 89.843593 Root MSE = 6.5646
------------------------------------------------------------------------------
write | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
female | 4.961672 .9408011 5.27 0.000 3.106039 6.817305
read | .3478027 .0599996 5.80 0.000 .2294597 .4661457
|
prog |
2 | 16.75438 6.793211 2.47 0.015 3.355472 30.15328
3 | 8.74215 6.838349 1.28 0.203 -4.745785 22.23009
|
socst | .475497 .1110706 4.28 0.000 .2564217 .6945723
|
prog#c.socst |
2 | -.300757 .1274851 -2.36 0.019 -.5522081 -.0493059
3 | -.210099 .1386112 -1.52 0.131 -.4834953 .0632973
|
_cons | 7.321844 5.673858 1.29 0.198 -3.869254 18.51294
------------------------------------------------------------------------------
estimates store m2
estimates table m1 m2, stats(N r2 r2_a) b(%6.3f) star
----------------------------------------
Variable | m1 m2
-------------+--------------------------
female | 4.916*** 4.962***
read | 0.343*** 0.348***
socst | 0.269*** 0.475***
|
prog |
2 | 0.998 16.754*
3 | -1.889 8.742
|
prog#c.socst |
2 | -0.301*
3 | -0.210
|
_cons | 18.069*** 7.322
-------------+--------------------------
N | 200 200
r2 | 0.524 0.537
r2_a | 0.512 0.520
----------------------------------------
legend: * p<0.05; ** p<0.01; *** p<0.001
estimates table m1 m2, stats(N r2 r2_a) se b(%6.3f) se(%6.3f)
----------------------------------
Variable | m1 m2
-------------+--------------------
female | 4.916 4.962
| 0.948 0.941
read | 0.343 0.348
| 0.060 0.060
socst | 0.269 0.475
| 0.058 0.111
|
prog |
2 | 0.998 16.754
| 1.233 6.793
3 | -1.889 8.742
| 1.389 6.838
|
prog#c.socst |
2 | -0.301
| 0.127
3 | -0.210
| 0.139
|
_cons | 18.069 7.322
| 3.069 5.674
-------------+--------------------
N | 200 200
r2 | 0.524 0.537
r2_a | 0.512 0.520
----------------------------------
legend: b/se
outreg2, see
(1) (2)
VARIABLES write write Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
female 4.916*** 4.962***
(0.948) (0.941)
read 0.343*** 0.348***
(0.0601) (0.0600)
2.prog 0.998 16.75**
(1.233) (6.793)
3.prog -1.889 8.742
(1.389) (6.838)
socst 0.269*** 0.475***
(0.0585) (0.111)
2.prog#c.socst -0.301**
(0.127)
3.prog#c.socst -0.210
(0.139)
Constant 18.07*** 7.322
(3.069) (5.674)
Observations 200 200
R-squared 0.524 0.537