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

name:  <unnamed>

> ass7.log

log type:  text

opened on:  31 Jan 2013, 13:34:46

. use "C:\Users\Michael\Documents\current class files\intro soc methods\cps_mar_2000_new with additional vars.dta", clear

. regress yrsed male if age>=25 & age<=34

Source |       SS       df       MS              Number of obs =   18538

-------------+------------------------------           F(  1, 18536) =   32.68

Model |  276.742433     1  276.742433           Prob > F      =  0.0000

Residual |  156979.922 18536  8.46892111           R-squared     =  0.0018

Total |  157256.664 18537  8.48339343           Root MSE      =  2.9101

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

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

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

male |  -.2444469   .0427623    -5.72   0.000    -.3282649   -.1606289

_cons |   13.55657   .0298401   454.31   0.000     13.49808    13.61506

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

. tabulate sex male

|         male

Sex |         0          1 |     Total

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

Male |         0     64,791 |    64,791

Female |    68,919          0 |    68,919

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

Total |    68,919     64,791 |   133,710

. tabulate sex female

|        female

Sex |         0          1 |     Total

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

Male |    64,791          0 |    64,791

Female |         0     68,919 |    68,919

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

Total |    64,791     68,919 |   133,710

. regress yrsed female if age>=25 & age<=34

Source |       SS       df       MS              Number of obs =   18538

-------------+------------------------------           F(  1, 18536) =   32.68

Model |  276.742433     1  276.742433           Prob > F      =  0.0000

Residual |  156979.922 18536  8.46892111           R-squared     =  0.0018

Total |  157256.664 18537  8.48339343           Root MSE      =  2.9101

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

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

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

female |   .2444469   .0427623     5.72   0.000     .1606289    .3282649

_cons |   13.31212   .0306297   434.62   0.000     13.25208    13.37216

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

. regress yrsed male if age>=25 & age<=34

Source |       SS       df       MS              Number of obs =   18538

-------------+------------------------------           F(  1, 18536) =   32.68

Model |  276.742433     1  276.742433           Prob > F      =  0.0000

Residual |  156979.922 18536  8.46892111           R-squared     =  0.0018

Total |  157256.664 18537  8.48339343           Root MSE      =  2.9101

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

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

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

male |  -.2444469   .0427623    -5.72   0.000    -.3282649   -.1606289

_cons |   13.55657   .0298401   454.31   0.000     13.49808    13.61506

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

* When we run the regressions with male compared to female, or female compared to male, what changes and what stays the same? The coefficient changes sign, and the constant change, so when the dummy variable is male the constant is female ed, and when the dummy variable is female the constant is male ed. The coefficient of the gender coefficient changes sign, the standard error stays the same, and the T-statistic changes sign. The R-squared (a measure of model fit) is the same in both of the above two models because the model is exactly the same, just the comparison category is different.

. table occ1990 if occ1990==178|occ1990==95|occ1990==125, contents(freq mean inctot)

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

Occupation, 1990      |

basis                 |        Freq.  mean(inctot)

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

Registered nurses |          966    40787.1677

Sociology instructors |            6   44363.33333

Lawyers |          441   99242.58277

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

. ttest inctot if occ1990==178 | occ1990==95, by(occ1990)

Two-sample t test with equal variances

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

Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]

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

Register |     966    40787.17    738.1285    22941.43    39338.65    42235.69

Lawyers |     441    99242.58    3421.936    71860.66    92517.21      105968

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

combined |    1407    59109.01    1388.629    52087.47    56385.01    61833.02

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

diff |           -58455.42    2556.381               -63470.15   -53440.68

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

diff = mean(Register) - mean(Lawyers)                         t = -22.8665

Ho: diff = 0                                     degrees of freedom =     1405

Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0

Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

* equal variance ttest for nurses compared to lawyers.

. regress inctot lawyers if occ1990==178 | occ1990==95

Source |       SS       df       MS              Number of obs =    1407

-------------+------------------------------           F(  1,  1405) =  522.88

Model |  1.0346e+12     1  1.0346e+12           Prob > F      =  0.0000

Residual |  2.7800e+12  1405  1.9787e+09           R-squared     =  0.2712

Total |  3.8146e+12  1406  2.7131e+09           Root MSE      =   44482

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

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

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

lawyers |   58455.42   2556.381    22.87   0.000     53440.68    63470.15

_cons |   40787.17   1431.192    28.50   0.000     37979.66    43594.67

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

* OLS regression for the same comparison. Notice how it’s the same?

. regress inctot nurses if occ1990==178 | occ1990==95

Source |       SS       df       MS              Number of obs =    1407

-------------+------------------------------           F(  1,  1405) =  522.88

Model |  1.0346e+12     1  1.0346e+12           Prob > F      =  0.0000

Residual |  2.7800e+12  1405  1.9787e+09           R-squared     =  0.2712

Total |  3.8146e+12  1406  2.7131e+09           Root MSE      =   44482

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

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

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

nurses |  -58455.42   2556.381   -22.87   0.000    -63470.15   -53440.68

_cons |   99242.58   2118.201    46.85   0.000     95087.41    103397.8

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

* nurses compared to lawyers.

. regress inctot nurses if occ1990==178 | occ1990==95 |occ1990==125

Source |       SS       df       MS              Number of obs =    1413

-------------+------------------------------           F(  1,  1411) =  513.39

Model |  1.0181e+12     1  1.0181e+12           Prob > F      =  0.0000

Residual |  2.7981e+12  1411  1.9830e+09           R-squared     =  0.2668

Total |  3.8161e+12  1412  2.7026e+09           Root MSE      =   44531

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

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

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

nurses |  -57718.78   2547.384   -22.66   0.000    -62715.85   -52721.71

_cons |   98505.95   2106.259    46.77   0.000     94374.21    102637.7

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

*nurses compared to the average of (lawyers and sociologists).

. regress inctot nurses sociologists if occ1990==178 | occ1990==95 |occ1990==125

Source |       SS       df       MS              Number of obs =    1413

-------------+------------------------------           F(  2,  1410) =  262.68

Model |  1.0359e+12     2  5.1795e+11           Prob > F      =  0.0000

Residual |  2.7802e+12  1410  1.9718e+09           R-squared     =  0.2715

Total |  3.8161e+12  1412  2.7026e+09           Root MSE      =   44405

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

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

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

nurses |  -58455.42   2551.942   -22.91   0.000    -63461.43    -53449.4

sociologists |  -54879.25   18251.16    -3.01   0.003    -90681.59   -19076.91

_cons |   99242.58   2114.522    46.93   0.000     95094.63    103390.5

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

* Here we have nurses and sociologists each compared to lawyers, but note: the std error and T-statistic is a little different here from the nurse-lawyer comparison we have above. Why? Well, adding the sociologists into the mix changes the everyone’s income variance in an equal variance test, so even the nurse-lawyer comparison is changed a little from the comparison we are used to, the one below.

. regress inctot nurses if occ1990==178 | occ1990==95

Source |       SS       df       MS              Number of obs =    1407

-------------+------------------------------           F(  1,  1405) =  522.88

Model |  1.0346e+12     1  1.0346e+12           Prob > F      =  0.0000

Residual |  2.7800e+12  1405  1.9787e+09           R-squared     =  0.2712

Total |  3.8146e+12  1406  2.7131e+09           Root MSE      =   44482

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

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

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

nurses |  -58455.42   2556.381   -22.87   0.000    -63470.15   -53440.68

_cons |   99242.58   2118.201    46.85   0.000     95087.41    103397.8

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

. regress inctot nurses if occ1990==178 | occ1990==95 |occ1990==125

Source |       SS       df       MS              Number of obs =    1413

-------------+------------------------------           F(  1,  1411) =  513.39

Model |  1.0181e+12     1  1.0181e+12           Prob > F      =  0.0000

Residual |  2.7981e+12  1411  1.9830e+09           R-squared     =  0.2668

Total |  3.8161e+12  1412  2.7026e+09           Root MSE      =   44531

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

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

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

nurses |  -57718.78   2547.384   -22.66   0.000    -62715.85   -52721.71

_cons |   98505.95   2106.259    46.77   0.000     94374.21    102637.7

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

* above, nurses compared to the average of (sociologists and lawyers)

. regress inctot nurses

Source |       SS       df       MS              Number of obs =  103226

-------------+------------------------------           F(  1,103224) =  207.52

Model |  2.1289e+11     1  2.1289e+11           Prob > F      =  0.0000

Residual |  1.0590e+14103224  1.0259e+09           R-squared     =  0.0020

Total |  1.0611e+14103225  1.0279e+09           Root MSE      =   32029

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

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

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

nurses |   14915.35   1035.387    14.41   0.000        12886    16944.69

_cons |   25871.82   100.1605   258.30   0.000     25675.51    26068.13

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

* nurses compared to all non-nurses

. log close

name:  <unnamed>