This page shows an example of the listcoef command with footnotes explaining the output using the elemapi2 data file.
We first use the elemapi2 data file and then first perform a regression analysis and include the beta option.
use https://stats.idre.ucla.edu/stat/stata/webbooks/reg/elemapi2
regress api00 ell meals yr_rnd mobility acs_k3 acs_46 full emer enroll, beta
Source | SS df MS Number of obs = 395
-------------+------------------------------ F( 9, 385) = 232.41
Model | 6740702.01 9 748966.89 Prob > F = 0.0000
Residual | 1240707.78 385 3222.61761 R-squared = 0.8446
-------------+------------------------------ Adj R-squared = 0.8409
Total | 7981409.79 394 20257.3852 Root MSE = 56.768
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api00 | Coef. Std. Err. t P>|t| Beta
-------------+----------------------------------------------------------------
ell | -.8600707 .2106317 -4.08 0.000 -.1495771
meals | -2.948216 .1703452 -17.31 0.000 -.6607003
yr_rnd | -19.88875 9.258442 -2.15 0.032 -.0591404
mobility | -1.301352 .4362053 -2.98 0.003 -.0686382
acs_k3 | 1.3187 2.252683 0.59 0.559 .0127287
acs_46 | 2.032456 .7983213 2.55 0.011 .0549752
full | .609715 .4758205 1.28 0.201 .0637969
emer | -.7066192 .6054086 -1.17 0.244 -.0580132
enroll | -.012164 .0167921 -0.72 0.469 -.0193554
_cons | 778.8305 61.68663 12.63 0.000 .
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The listcoef command can then be used after the regress command to show several types of standardized regression coefficients.
listcoef
regress (N=395a): Unstandardized and Standardized Estimates
Observed SD: 142.32844b
SD of Error: 56.768104c
---------------------------------------------------------------------------
api00d| be tf P>|t|f bStdXg bStdYh bStdXYi SDofXj
---------+-----------------------------------------------------------------
ell | -0.86007 -4.083 0.000 -21.2891 -0.0060 -0.1496 24.7527
meals | -2.94822 -17.307 0.000 -94.0364 -0.0207 -0.6607 31.8960
yr_rnd | -19.88875 -2.148 0.032 -8.4174 -0.1397 -0.0591 0.4232
mobility | -1.30135 -2.983 0.003 -9.7692 -0.0091 -0.0686 7.5069
acs_k3 | 1.31870 0.585 0.559 1.8117 0.0093 0.0127 1.3738
acs_46 | 2.03246 2.546 0.011 7.8245 0.0143 0.0550 3.8498
full | 0.60972 1.281 0.201 9.0801 0.0043 0.0638 14.8924
emer | -0.70662 -1.167 0.244 -8.2569 -0.0050 -0.0580 11.6851
enroll | -0.01216 -0.724 0.469 -2.7548 -0.0001 -0.0194 226.4732
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Footnotes
a. This is the number of observations that were used in the regression and hence, in the calculation of the coefficients given in the listcoef output.
b. This is the observed standard deviation; in other words, the standard deviation of the y-variable (also known as the dependent variable), in this case, api00.
c. This is the standard deviation of the error (in other words, the standard deviation of the error term). You will notice that it is the same as the Root MSE listed in the regression output. This term is also known as the standard error of prediction.
d. This is the dependent variable. Listed below it are all of the independent variables in the model.
e. These are the unstandardized regression coefficients. They are the same coefficients that are listed in the regression output in the column labeled coef.
f. These are the same t-tests and
p-values that are listed in the regression output. The columns in both
outputs are labeled the same. These
columns provide the t value and 2 tailed p value used in testing the null
hypothesis that the coefficient/parameter is 0. If you use a 2
tailed test, then you would compare each p value to your preselected value
of alpha. Coefficients having p values less than alpha are
significant. For example, if you chose alpha to be 0.05,
coefficients having a p value of 0.05 or less would be statistically
significant (i.e. you can reject the null hypothesis and say that the
coefficient is significantly different from 0). If you use a 1
tailed test (i.e. you predict that the parameter will go in a particular
direction), then you can divide the p value by 2 before comparing it to
your preselected alpha level. With a 2 tailed test and alpha of
0.05, you can reject the null hypothesis that the coefficient for ell is equal to 0. The coefficient of
-.86 is significantly different from 0. Using a 2 tailed test and alpha of 0.01, the p value of
0.000 is smaller than 0.01 and the coefficient for ell would still be
significant at the 0.01 level. Had you predicted that this coefficient
would be positive (i.e. a one tail test), you would be able to divide the
p value by 2 before comparing it to alpha. This would yield a one
tailed p value of 0.000, which is less than 0.01 and then you could
conclude that this coefficient is greater than 0 with a one tailed alpha
of 0.01.
The coefficient for meals is significantly
different from 0 using alpha of 0.05 because its p value of 0.000 is
smaller than 0.05.
The coefficient for yr_rnd (-19.89) is
significantly different from 0 because its p value is definitely smaller
than 0.05 and even 0.01.
The coefficient for mobility is significantly
different from 0 using alpha of 0.05 because its p value of 0.003 is
smaller than 0.05.
The coefficient for acs_k3 is not significantly different
from 0 using alpha of 0.05 because its p value of .559 is greater than
0.05.
The coefficient for acs_46 is significantly
different from 0 using alpha of 0.05 because its p value of 0.011 is
smaller than 0.05.
g. These are the regression coefficients with the x-variables (the independent variables) in standard deviations and the y-variable (the dependent variable) in its original units.
h. These are the regression coefficients with the x-variables (the independent variables) in original units and the y-variable (the dependent variable) in standard deviations.
i. These are the regression coefficients with both the x-variable (the independent variable) and the y-variable (the dependent variable) in standard deviations. You will notice that these are the same values given in the Beta column of the regression output.
j. This is the standard deviation of the x-variables (the dependent variables).