Stata Technical Bulletin
29
. regress job fem phd ment fel art cit
Source I |
SS |
df |
MS |
Number of obs = 408 T? / Л ЛЛП — *7 70 | ||
— |
1 ∖ Vt *±V ɪ √ |
— -L I . I Q | ||||
Model I |
81.0584763 |
6 13. |
5097461 |
Prob > F |
= 0.0000 | |
Residual ∣ |
304.737915 |
401 .759944926 |
R-Squared |
= 0.2101 | ||
Adj R-Squared |
= 0.1983 | |||||
— | ||||||
Total I |
385.796392 |
407 .947902683 |
Root MSE |
= .87175 | ||
— job I |
Coef. |
Std. Err. |
t |
p>∣t∣ |
[957. Conf. |
— Interval] |
---------+- |
— | |||||
fem I |
-.1391939 |
.0902344 |
-1.543 |
0.124 |
-.3165856 |
.0381977 |
phd I |
.2726826 |
.0493183 |
5.529 |
0.000 |
.1757278 |
.3696375 |
ment I |
.0011867 |
.0007012 |
1.692 |
0.091 |
-.0001917 |
.0025651 |
fel I |
.2341384 |
.0948206 |
2.469 |
0.014 |
.0477308 |
.4205461 |
art I |
.0228011 |
.0288843 |
0.789 |
0.430 |
-.0339824 |
.0795846 |
cit I |
.0044788 |
.0019687 |
2.275 |
0.023 |
.0006087 |
.008349 |
_cons I |
1.067184 |
.1661357 |
6.424 |
0.000 |
.7405785 |
1.39379 |
Iistcoef provides additional information:
. Iistcoef, help std constant
regress (N=408): Unstandardized and Standardized Estimates
Observed SD: .97360294
SD of Error: .8717482
job I |
b |
t |
p>∣t∣ |
bStdX |
bStdY |
bStdXY |
SDofX |
— | |||||||
fem I |
-0.13919 |
-1.543 |
0.124 |
-0.0680 |
-0.1430 |
-0.0698 |
0.4883 |
phd I |
0.27268 |
5.529 |
0.000 |
0.2601 |
0.2801 |
0.2671 |
0.9538 |
ment I |
0.00119 |
1.692 |
0.091 |
0.0778 |
0.0012 |
0.0799 |
65.5299 |
fel I |
0.23414 |
2.469 |
0.014 |
0.1139 |
0.2405 |
0.1170 |
0.4866 |
art I |
0.02280 |
0.789 |
0.430 |
0.0514 |
0.0234 |
0.0528 |
2.2561 |
cit I |
0.00448 |
2.275 |
0.023 |
0.1481 |
0.0046 |
0.1521 |
33.0599 |
„cons I |
1.06718 |
6.424 |
0.000 | ||||
— b = |
raw coefficient |
— | |||||
t = |
t-score for |
test of |
b=0 | ||||
p>∣t∣ = |
p-value for |
t-test |
bStdX = х-standardized coefficient
bStdY = у-standardized coefficient
bStdXY = fully standardized coefficient
SDofX = standard deviation of X
Example with logit
The logit model illustrates that Iistcoef can be used to obtain alternative transformations of the basic parameters. We
begin by estimating the logit model, which produces the standard output:
. logit Ifp k5 k618 age we he Iwg inct nolog Logit estimates Log likelihood = -452.63296 |
Number of obs = Prob > chi2 = Pseudo R2 = |
753 | ||||
— Ifp I |
Coef. |
Std. Err. |
z |
P>∣z∣ |
[957. Conf. |
— Interval] |
— | ||||||
кБ I |
-1.462913 |
. 1970006 |
-7.426 |
0.000 |
-1.849027 |
-1.076799 |
k618 I |
-.0645707 |
.0680008 |
-0.950 |
0.342 |
-.1978499 |
.0687085 |
age I |
-.0628706 |
.0127831 |
-4.918 |
0.000 |
-.0879249 |
-.0378162 |
wc I |
.8072738 |
.2299799 |
3.510 |
0.000 |
.3565215 |
1.258026 |
he I |
.1117336 |
.2060397 |
0.542 |
0.588 |
-.2920969 |
.515564 |
Iwg I |
.6046931 |
.1508176 |
4.009 |
0.000 |
.3090961 |
.9002901 |
inc I |
-.0344464 |
.0082084 |
-4.196 |
0.000 |
-.0505346 |
-.0183583 |
_cons I |
3.18214 |
.6443751 |
4.938 |
0.000 |
1.919188 |
4.445092 |
Most frequently, the logit model is interpreted using factor change coefficients, also known as odds ratios. These are the default
option for Iistcoef.
. Iistcoef, help
More intriguing information
1. Micro-strategies of Contextualization Cross-national Transfer of Socially Responsible Investment2. A Brief Introduction to the Guidance Theory of Representation
3. Mean Variance Optimization of Non-Linear Systems and Worst-case Analysis
4. The name is absent
5. Industrial districts, innovation and I-district effect: territory or industrial specialization?
6. A production model and maintenance planning model for the process industry
7. The name is absent
8. The name is absent
9. The name is absent
10. Chebyshev polynomial approximation to approximate partial differential equations