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      name:  <unnamed>

       log:  C:\Users\mexmi\Documents\newer web pages\soc_meth_proj3\Soc180B_spr2019_logs\class10_

> log.log

  log type:  text

 opened on:   2 May 2019, 13:27:44

 

. use "C:\Users\mexmi\Desktop\cps_mar_2000_new.dta", clear

. *class starts here

 

*In class we talked about how to fill in the big regression table in HW3, Q1.

* First you need to create variables for Vietnam veteran and age squared.

 

. gen age_sq=age^2

 

. codebook vetlast

 

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vetlast                                                    Veteran's most recent period of service

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                  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)

 

. label define vietnam_vet_lbl 0 "not Vietnam vet" 1 "Vietnam vet"

 

* You don’t need the labels for Vietnam vet, but I find them helpful.

 

. label val vietnam_vet vietnam_vet_lbl

 

. tabulate vetlast vietnam_vet

 

Veteran's most recent |      vietnam_vet

    period of service | not Vietn  Vietnam v |     Total

----------------------+----------------------+----------

                  NIU |    30,904          0 |    30,904

           No service |    91,149          0 |    91,149

         World War II |     2,428          0 |     2,428

           Korean War |     1,716          0 |     1,716

          Vietnam Era |         0      3,683 |     3,683

        Other service |     3,830          0 |     3,830

----------------------+----------------------+----------

                Total |   130,027      3,683 |   133,710

 

* When you make a new variable, always tabulate it against the old variable, just to make sure you have coded it correctly. It looks alright here!

 

 

* Now I am going to generate the regression equation for model 5, HW3, Q1:

 

. regress incwage vietnam_vet age age_sq i.sex yrsed if age>=25 & age<=64 [aweight= perwt_rounded]

(sum of wgt is   1.4261e+08)

 

      Source |       SS       df       MS              Number of obs =   69305

-------------+------------------------------           F(  5, 69299) = 3127.96

       Model |  1.3427e+13     5  2.6853e+12           Prob > F      =  0.0000

    Residual |  5.9492e+13 69299   858488914           R-squared     =  0.1841

-------------+------------------------------           Adj R-squared =  0.1841

       Total |  7.2919e+13 69304  1.0522e+09           Root MSE      =   29300

 

------------------------------------------------------------------------------

     incwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

-------------+----------------------------------------------------------------

 vietnam_vet |    1035.18   532.7493     1.94   0.052    -9.007979    2079.367

         age |   2848.096   87.34381    32.61   0.000     2676.902     3019.29

      age_sq |  -31.92762   .9924702   -32.17   0.000    -33.87286   -29.98238

             |

         sex |

     Female  |  -16607.58   228.9415   -72.54   0.000     -17056.3   -16158.85

       yrsed |   3540.933   38.50133    91.97   0.000      3465.47    3616.395

       _cons |  -71687.23   1898.127   -37.77   0.000    -75407.55    -67966.9

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* icnwage is the dependent variable, or outcome variable, and it comes after “regress” in every model.

 

* You should use the above syntax for M5, and then reduce the predictor variable set to generate M4, M3, M2, and M1, thusly:

 

M4: regress incwage vietnam_vet age age_sq i.sex if age>=25 & age<=64 [aweight= perwt_rounded]

M3: regress incwage vietnam_vet age i.sex if age>=25 & age<=64 [aweight= perwt_rounded]

M2: regress incwage vietnam_vet i.sex if age>=25 & age<=64 [aweight= perwt_rounded]

M1: regress incwage vietnam_vet if age>=25 & age<=64 [aweight= perwt_rounded]

 

Note that the age filter (the “if age>=25 & age<=64” part is the same in every model. And note that the [aweight= perwt_rounded] is always present as well. The number of observations, n, is =69305 across all models M1-M5 if you have entered the commands correctly.

 

 

 

 

. log close

      name:  <unnamed>

       log:  C:\Users\mexmi\Documents\newer web pages\soc_meth_proj3\Soc180B_spr2019_logs\class10_

> log.log

  log type:  text

 closed on:   2 May 2019, 16:21:55

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