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name: <unnamed>
log: C:\Documents and Settings\Michael Rosenfeld\My Documents\newer web pages\soc_meth_proj3\2010_logs\section_five.log
log type: text
opened on: 23 Feb 2010, 12:00:57
. *a new free update is available for stata. Here, below is what it looks like when you update Stata:
. update all
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> update ado
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copying StataSE.exe to StataSE_old.exe
(!) 3. Stata is about to shutdown this version of
Stata and automatically launch the new version.
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name: <unnamed>
log: C:\Documents and Settings\Michael Rosenfeld\My Documents\newer web pages\soc
> _meth_proj3\2010_logs\section_five.log
log type: text
opened on: 23 Feb 2010, 12:07:12
. use "C:\Documents and Settings\Michael Rosenfeld\Desktop\cps_mar_2000_new.dta", clear
. codebook vetlast
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vetlast Veteran's most recent period of service
---------------------------------------------------------------------------------
type: numeric (byte)
label: vetlastlbl
range: [0,9] units: 1
unique values: 6 missing .: 0/133710
tabulation: Freq. Numeric Label
30904 0 NIU
91149 1 No service
2428 4 World War II
1716 6 Korean War
3683 8 Vietnam Era
3830 9 Other service
. gen vietnam_vet=0
. replace vietnam_vet=1 if vetlast==8
(3683 real changes made)
. regress incwage vietnam_vet if age >=25 & age<=64 [aweight= perwt_rounded]
(sum of wgt is 1.4261e+08)
Source | SS df MS Number of obs = 69305
-------------+------------------------------ F( 1, 69303) = 513.59
Model | 5.3641e+11 1 5.3641e+11 Prob > F = 0.0000
Residual | 7.2383e+13 69303 1.0444e+09 R-squared = 0.0074
-------------+------------------------------ Adj R-squared = 0.0073
Total | 7.2919e+13 69304 1.0522e+09 Root MSE = 32318
------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
vietnam_vet | 12634.41 557.5048 22.66 0.000 11541.7 13727.11
_cons | 26818.78 126.0225 212.81 0.000 26571.78 27065.79
------------------------------------------------------------------------------
. regress incwage vietnam_vet male if age >=25 & age<=64 [aweight= perwt_rounded]
(sum of wgt is 1.4261e+08)
Source | SS df MS Number of obs = 69305
-------------+------------------------------ F( 2, 69302) = 2547.15
Model | 4.9931e+12 2 2.4966e+12 Prob > F = 0.0000
Residual | 6.7926e+13 69302 980142991 R-squared = 0.0685
-------------+------------------------------ Adj R-squared = 0.0684
Total | 7.2919e+13 69304 1.0522e+09 Root MSE = 31307
------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
vietnam_vet | 4216.537 554.3126 7.61 0.000 3130.085 5302.989
male | 16465.13 244.1752 67.43 0.000 15986.55 16943.71
_cons | 19198.75 166.3547 115.41 0.000 18872.69 19524.8
------------------------------------------------------------------------------
. table male vietnam_vet if age>=25 & age<=64 [aweight=perwt_rounded], contents(freq mean incwage)
------------------------------------
| vietnam_vet
male | 0 1
----------+-------------------------
female | 35,603 99
| 19177.51915 31576.67305
|
male | 30,113 3,490
| 35688.51837 39663.00843
------------------------------------
* A student asks why the constant term in the above regression is not exactly equal to the female non-vet actual mean income. The answer (it takes me a bit to figure out) is that the predicted female non-vet income is not the same as the actual female non-vet income.
. table male vietnam_vet if age>=25 & age<=64 [aweight=perwt_rounded], contents(freq mean incwage) row col
-------------------------------------------------
| vietnam_vet
male | 0 1 Total
----------+--------------------------------------
female | 35,603 99 35,702
| 19177.51915 31576.67305 19209.68506
|
male | 30,113 3,490 33,603
| 35688.51837 39663.00843 36093.11552
|
Total | 65,716 3,589 69,305
| 26818.78466 39453.18996 27464.36983
-------------------------------------------------
. regress incwage vietnam_vet male if age >=25 & age<=64
Source | SS df MS Number of obs = 69305
-------------+------------------------------ F( 2, 69302) = 2527.09
Model | 4.7473e+12 2 2.3736e+12 Prob > F = 0.0000
Residual | 6.5094e+13 69302 939274658 R-squared = 0.0680
-------------+------------------------------ Adj R-squared = 0.0679
Total | 6.9841e+13 69304 1.0077e+09 Root MSE = 30648
------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
vietnam_vet | 3775.139 539.569 7.00 0.000 2717.585 4832.693
male | 16098.32 239.2401 67.29 0.000 15629.41 16567.23
_cons | 18601.47 162.2066 114.68 0.000 18283.55 18919.4
------------------------------------------------------------------------------
. table male vietnam_vet if age>=25 & age<=64, contents(freq mean incwage) row col
-------------------------------------------------
| vietnam_vet
male | 0 1 Total
----------+--------------------------------------
female | 35,603 99 35,702
| 18582.58043 29170.12121 18611.93919
|
male | 30,113 3,490 33,603
| 34722.12148 38282.21633 35091.87212
|
Total | 65,716 3,589 69,305
| 25978.19184 38030.8657 26602.34661
-------------------------------------------------
. regress incwage vietnam_vet male if age >=25 & age<=64 & incwage!=. [aweight= perwt_rounded]
(sum of wgt is 1.4261e+08)
Source | SS df MS Number of obs = 69305
-------------+------------------------------ F( 2, 69302) = 2547.15
Model | 4.9931e+12 2 2.4966e+12 Prob > F = 0.0000
Residual | 6.7926e+13 69302 980142991 R-squared = 0.0685
-------------+------------------------------ Adj R-squared = 0.0684
Total | 7.2919e+13 69304 1.0522e+09 Root MSE = 31307
------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
vietnam_vet | 4216.537 554.3126 7.61 0.000 3130.085 5302.989
male | 16465.13 244.1752 67.43 0.000 15986.55 16943.71
_cons | 19198.75 166.3547 115.41 0.000 18872.69 19524.8
------------------------------------------------------------------------------
. table male vietnam_vet if age>=25 & age<=64 & incwage!=. [aweight=perwt_rounded], contents(freq mean incwage) row col
-------------------------------------------------
| vietnam_vet
male | 0 1 Total
----------+--------------------------------------
female | 35,603 99 35,702
| 19177.51915 31576.67305 19209.68506
|
male | 30,113 3,490 33,603
| 35688.51837 39663.00843 36093.11552
|
Total | 65,716 3,589 69,305
| 26818.78466 39453.18996 27464.36983
-------------------------------------------------
. regress incwage vietnam_vet male if age >=25 & age<=64 [aweight= perwt_rounded]
(sum of wgt is 1.4261e+08)
Source | SS df MS Number of obs = 69305
-------------+------------------------------ F( 2, 69302) = 2547.15
Model | 4.9931e+12 2 2.4966e+12 Prob > F = 0.0000
Residual | 6.7926e+13 69302 980142991 R-squared = 0.0685
-------------+------------------------------ Adj R-squared = 0.0684
Total | 7.2919e+13 69304 1.0522e+09 Root MSE = 31307
------------------------------------------------------------------------------
incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
vietnam_vet | 4216.537 554.3126 7.61 0.000 3130.085 5302.989
male | 16465.13 244.1752 67.43 0.000 15986.55 16943.71
_cons | 19198.75 166.3547 115.41 0.000 18872.69 19524.8
------------------------------------------------------------------------------
. predict M2
(option xb assumed; fitted values)
. table male vietnam_vet if age>=25 & age<=64 & incwage!=. [aweight=perwt_rounded], contents(freq mean incwage mean M2) row col
-------------------------------------------------
| vietnam_vet
male | 0 1 Total
----------+--------------------------------------
female | 35,603 99 35,702
| 19177.51915 31576.67305 19209.68506
| 19198.75 23415.28 19209.69
|
male | 30,113 3,490 33,603
| 35688.51837 39663.00843 36093.11552
| 35663.88 39880.41 36093.12
|
Total | 65,716 3,589 69,305
| 26818.78466 39453.18996 27464.36983
| 26818.79 39453.19 27464.37
-------------------------------------------------
. *the constant equals the predicted value for non-vet women, not the actual value. Why are they not the same?
. *In order to fit the four cells exactly, we would need 4 terms in the model.
. desmat: regress incwage vietnam_vet*male if age >=25 & age<=64 [aweight= perwt_rounded]
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Linear regression
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Dependent variable incwage
Number of observations: 69305
aweight: perwt_rounded
F statistic: 1700.382
Model degrees of freedom: 3
Residual degrees of freedom: 69301
R-squared: 0.069
Adjusted R-squared: 0.069
Root MSE 31306.005
Prob: 0.000
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nr Effect Coeff s.e.
---------------------------------------------------------------------------------
vietnam_vet
1 1 12399.154** 3270.123
male
2 male 16510.999** 244.833
vietnam_vet.male
3 1.male -8424.664* 3318.137
4 _cons 19177.519** 166.558
---------------------------------------------------------------------------------
* p < .05
** p < .01
. *Now the predicted values of the model and the actual values will fit exactly in the 4 cells of gender and vietnam vet status
. predict M2_plus
(option xb assumed; fitted values)
* predict generates a new variable with the predicted values from the last regression we just ran.
. table male vietnam_vet if age>=25 & age<=64 & incwage!=. [aweight=perwt_rounded], contents(freq mean incwage mean M2 mean M2_plus) row col
-------------------------------------------------
| vietnam_vet
male | 0 1 Total
----------+--------------------------------------
female | 35,603 99 35,702
| 19177.51915 31576.67305 19209.68506
| 19198.75 23415.28 19209.69
| 19177.52 31576.67 19209.69
|
male | 30,113 3,490 33,603
| 35688.51837 39663.00843 36093.11552
| 35663.88 39880.41 36093.12
| 35688.52 39663.01 36093.12
|
Total | 65,716 3,589 69,305
| 26818.78466 39453.18996 27464.36983
| 26818.79 39453.19 27464.37
| 26818.79 39453.19 27464.37
-------------------------------------------------
* This regression fits all 4 cells exactly, because it included 4 terms…
. drop _*
. save "C:\Documents and Settings\Michael Rosenfeld\Desktop\cps_mar_2000_new.dta"
> , replace
file C:\Documents and Settings\Michael Rosenfeld\Desktop\cps_mar_2000_new.dta sav
> ed
. exit, clear