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name: <unnamed>
log: C:\Users\Michael\Documents\newer web pages\soc_meth_proj3\fall_2016_logs\class18.log
log type: text
opened on: 30 Nov 2016, 10:13:07
. use "C:\Users\Michael\Documents\current class files\intro soc methods\cps_mar_2000_new with additional vars.dta",
> clear
. regress incwage male ib3.metro yrsed lawyers
Source | SS df MS Number of obs = 103226
-------------+------------------------------ F( 7,103218) = 3235.73
Model | 1.5454e+13 7 2.2077e+12 Prob > F = 0.0000
Residual | 7.0423e+13103218 682277869 R-squared = 0.1800
-------------+------------------------------ Adj R-squared = 0.1799
Total | 8.5877e+13103225 831940347 Root MSE = 26120
----------------------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
male | 12144.91 162.8297 74.59 0.000 11825.76 12464.05
|
metro |
Not identifiable | -1735.335 1600.97 -1.08 0.278 -4873.214 1402.545
Not in metro area | -6042.26 216.8909 -27.86 0.000 -6467.364 -5617.157
Central city | -2266.333 211.3957 -10.72 0.000 -2680.666 -1852
Central city status unknown | -3106.574 248.5734 -12.50 0.000 -3593.774 -2619.373
|
yrsed | 3038.551 25.93063 117.18 0.000 2987.727 3089.374
lawyers | 38622.84 1252.251 30.84 0.000 36168.45 41077.24
_cons | -22955.05 370.3499 -61.98 0.000 -23680.94 -22229.17
----------------------------------------------------------------------------------------------
. regress incwage female ib3.metro yrsed lawyers
Source | SS df MS Number of obs = 103226
-------------+------------------------------ F( 7,103218) = 3235.73
Model | 1.5454e+13 7 2.2077e+12 Prob > F = 0.0000
Residual | 7.0423e+13103218 682277869 R-squared = 0.1800
-------------+------------------------------ Adj R-squared = 0.1799
Total | 8.5877e+13103225 831940347 Root MSE = 26120
----------------------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
female | -12144.91 162.8297 -74.59 0.000 -12464.05 -11825.76
|
metro |
Not identifiable | -1735.335 1600.97 -1.08 0.278 -4873.214 1402.545
Not in metro area | -6042.26 216.8909 -27.86 0.000 -6467.364 -5617.157
Central city | -2266.333 211.3957 -10.72 0.000 -2680.666 -1852
Central city status unknown | -3106.574 248.5734 -12.50 0.000 -3593.774 -2619.373
|
yrsed | 3038.551 25.93063 117.18 0.000 2987.727 3089.374
lawyers | 38622.84 1252.251 30.84 0.000 36168.45 41077.24
_cons | -10810.15 372.5182 -29.02 0.000 -11540.28 -10080.02
----------------------------------------------------------------------------------------------
*Notice what changes and what does not change with the change of comparison category from male to female.
. codebook metro
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metro Metropolitan central city status
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type: numeric (byte)
label: metrolbl
range: [0,4] units: 1
unique values: 5 missing .: 0/133710
tabulation: Freq. Numeric Label
340 0 Not identifiable
29658 1 Not in metro area
32481 2 Central city
51468 3 Outside central city
19763 4 Central city status unknown
. lincom 2.metro-1.metro
( 1) - 1.metro + 2.metro = 0
------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | 3775.927 238.677 15.82 0.000 3308.124 4243.731
------------------------------------------------------------------------------
. regress incwage female ib1.metro yrsed lawyers
Source | SS df MS Number of obs = 103226
-------------+------------------------------ F( 7,103218) = 3235.73
Model | 1.5454e+13 7 2.2077e+12 Prob > F = 0.0000
Residual | 7.0423e+13103218 682277869 R-squared = 0.1800
-------------+------------------------------ Adj R-squared = 0.1799
Total | 8.5877e+13103225 831940347 Root MSE = 26120
----------------------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
female | -12144.91 162.8297 -74.59 0.000 -12464.05 -11825.76
|
metro |
Not identifiable | 4306.926 1604.846 2.68 0.007 1161.448 7452.404
Central city | 3775.927 238.677 15.82 0.000 3308.124 4243.731
Outside central city | 6042.26 216.8909 27.86 0.000 5617.157 6467.364
Central city status unknown | 2935.687 272.1688 10.79 0.000 2402.239 3469.134
|
yrsed | 3038.551 25.93063 117.18 0.000 2987.727 3089.374
lawyers | 38622.84 1252.251 30.84 0.000 36168.45 41077.24
_cons | -16852.41 374.9396 -44.95 0.000 -17587.28 -16117.53
----------------------------------------------------------------------------------------------
* notice what changes and what doesn’t change when we change the comparison category for metro.
. regress incwage female ib1.metro months_ed lawyers
Source | SS df MS Number of obs = 103226
-------------+------------------------------ F( 7,103218) = 3235.73
Model | 1.5454e+13 7 2.2077e+12 Prob > F = 0.0000
Residual | 7.0423e+13103218 682277869 R-squared = 0.1800
-------------+------------------------------ Adj R-squared = 0.1799
Total | 8.5877e+13103225 831940347 Root MSE = 26120
----------------------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
female | -12144.91 162.8297 -74.59 0.000 -12464.05 -11825.76
|
metro |
Not identifiable | 4306.926 1604.846 2.68 0.007 1161.448 7452.404
Central city | 3775.927 238.677 15.82 0.000 3308.124 4243.731
Outside central city | 6042.26 216.8909 27.86 0.000 5617.157 6467.364
Central city status unknown | 2935.687 272.1688 10.79 0.000 2402.239 3469.134
|
months_ed | 253.2126 2.160886 117.18 0.000 248.9773 257.4479
lawyers | 38622.84 1252.251 30.84 0.000 36168.45 41077.24
_cons | -16852.41 374.9396 -44.95 0.000 -17587.28 -16117.53
----------------------------------------------------------------------------------------------
*and when we rescale yrsed to months_ed
. regress incwage female ib1.metro yrsed lawyers
Source | SS df MS Number of obs = 103226
-------------+------------------------------ F( 7,103218) = 3235.73
Model | 1.5454e+13 7 2.2077e+12 Prob > F = 0.0000
Residual | 7.0423e+13103218 682277869 R-squared = 0.1800
-------------+------------------------------ Adj R-squared = 0.1799
Total | 8.5877e+13103225 831940347 Root MSE = 26120
----------------------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
female | -12144.91 162.8297 -74.59 0.000 -12464.05 -11825.76
|
metro |
Not identifiable | 4306.926 1604.846 2.68 0.007 1161.448 7452.404
Central city | 3775.927 238.677 15.82 0.000 3308.124 4243.731
Outside central city | 6042.26 216.8909 27.86 0.000 5617.157 6467.364
Central city status unknown | 2935.687 272.1688 10.79 0.000 2402.239 3469.134
|
yrsed | 3038.551 25.93063 117.18 0.000 2987.727 3089.374
lawyers | 38622.84 1252.251 30.84 0.000 36168.45 41077.24
_cons | -16852.41 374.9396 -44.95 0.000 -17587.28 -16117.53
----------------------------------------------------------------------------------------------
*and when we add a variable with many missing values.
. regress incwage female ib1.metro yrsed lawyers i.union_new
Source | SS df MS Number of obs = 13461
-------------+------------------------------ F( 9, 13451) = 388.81
Model | 2.4445e+12 9 2.7161e+11 Prob > F = 0.0000
Residual | 9.3965e+12 13451 698569626 R-squared = 0.2064
-------------+------------------------------ Adj R-squared = 0.2059
Total | 1.1841e+13 13460 879714936 Root MSE = 26430
----------------------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
female | -14307.7 457.4747 -31.28 0.000 -15204.41 -13410.98
|
metro |
Not identifiable | 468.7232 3886.338 0.12 0.904 -7149.044 8086.49
Central city | 4114.894 679.7459 6.05 0.000 2782.497 5447.292
Outside central city | 7639.386 610.9974 12.50 0.000 6441.745 8837.026
Central city status unknown | 3442.571 770.9896 4.47 0.000 1931.323 4953.819
|
yrsed | 3661.079 84.63333 43.26 0.000 3495.186 3826.973
lawyers | 40264.6 2878.157 13.99 0.000 34623 45906.19
|
union_new |
2 | 4491.669 660.7497 6.80 0.000 3196.507 5786.831
3 | -1968.3 1911.042 -1.03 0.303 -5714.21 1777.61
|
_cons | -17048.49 1232.921 -13.83 0.000 -19465.19 -14631.79
----------------------------------------------------------------------------------------------
. codebook union
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union Union membership
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type: numeric (byte)
label: unionlbl
range: [0,3] units: 1
unique values: 4 missing .: 0/133710
tabulation: Freq. Numeric Label
1.2e+05 0 NIU
11383 1 No union coverage
1883 2 Member of labor union
195 3 Covered by union but not a
member
. tabulate union_new, miss
union_new | Freq. Percent Cum.
------------+-----------------------------------
1 | 11,383 8.51 8.51
2 | 1,883 1.41 9.92
3 | 195 0.15 10.07
. | 120,249 89.93 100.00
------------+-----------------------------------
Total | 133,710 100.00
. log close
name: <unnamed>
log: C:\Users\Michael\Documents\newer web pages\soc_meth_proj3\fall_2016_logs\class18.log
log type: text
closed on: 30 Nov 2016, 12:52:39
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