Stata Textbook Examples
Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge (1st & 2nd eds.)
Chapter 7 - Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables

Example 7.1: Hourly Wage Equation

use http://fmwww.bc.edu/ec-p/data/wooldridge/wage1
reg wage female educ exper tenure

      Source |       SS       df       MS              Number of obs =     526
-------------+------------------------------           F(  4,   521) =   74.40
       Model |  2603.10658     4  650.776644           Prob > F      =  0.0000
    Residual |  4557.30771   521   8.7472317           R-squared     =  0.3635
-------------+------------------------------           Adj R-squared =  0.3587
       Total |  7160.41429   525  13.6388844           Root MSE      =  2.9576

------------------------------------------------------------------------------
        wage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      female |  -1.810852   .2648252    -6.84   0.000    -2.331109   -1.290596
        educ |   .5715048   .0493373    11.58   0.000     .4745803    .6684293
       exper |   .0253959   .0115694     2.20   0.029     .0026674    .0481243
      tenure |   .1410051   .0211617     6.66   0.000     .0994323    .1825778
       _cons |  -1.567939   .7245511    -2.16   0.031    -2.991339    -.144538
------------------------------------------------------------------------------
reg wage female

      Source |       SS       df       MS              Number of obs =     526
-------------+------------------------------           F(  1,   524) =   68.54
       Model |  828.220467     1  828.220467           Prob > F      =  0.0000
    Residual |  6332.19382   524  12.0843394           R-squared     =  0.1157
-------------+------------------------------           Adj R-squared =  0.1140
       Total |  7160.41429   525  13.6388844           Root MSE      =  3.4763

------------------------------------------------------------------------------
        wage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      female |   -2.51183   .3034092    -8.28   0.000    -3.107878   -1.915782
       _cons |   7.099489   .2100082    33.81   0.000     6.686928     7.51205
------------------------------------------------------------------------------

Average wage for women

lincom female+_cons

 ( 1)  female + _cons = 0.0

------------------------------------------------------------------------------
        wage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   4.587659   .2189834    20.95   0.000     4.157466    5.017852
------------------------------------------------------------------------------

Example 7.2: Effects of Computer Ownership on College GPA

use http://fmwww.bc.edu/ec-p/data/wooldridge/gpa1
reg colGPA PC hsGPA ACT

      Source |       SS       df       MS              Number of obs =     141
-------------+------------------------------           F(  3,   137) =   12.83
       Model |  4.25741863     3  1.41913954           Prob > F      =  0.0000
    Residual |  15.1486808   137  .110574313           R-squared     =  0.2194
-------------+------------------------------           Adj R-squared =  0.2023
       Total |  19.4060994   140  .138614996           Root MSE      =  .33253

------------------------------------------------------------------------------
      colGPA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          PC |   .1573092   .0572875     2.75   0.007     .0440271    .2705913
       hsGPA |   .4472417   .0936475     4.78   0.000     .2620603     .632423
         ACT |    .008659   .0105342     0.82   0.413    -.0121717    .0294897
       _cons |    1.26352   .3331255     3.79   0.000     .6047871    1.922253
------------------------------------------------------------------------------
reg colGPA PC

      Source |       SS       df       MS              Number of obs =     141
-------------+------------------------------           F(  1,   139) =    7.31
       Model |  .970092892     1  .970092892           Prob > F      =  0.0077
    Residual |  18.4360066   139  .132633141           R-squared     =  0.0500
-------------+------------------------------           Adj R-squared =  0.0432
       Total |  19.4060994   140  .138614996           Root MSE      =  .36419

------------------------------------------------------------------------------
      colGPA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          PC |   .1695168   .0626805     2.70   0.008     .0455864    .2934472
       _cons |   2.989412   .0395018    75.68   0.000      2.91131    3.067514
-----------------------------------------------------------------------------

Example 7.3: Effects of Training Grants on Hours of Training in 1988

use http://fmwww.bc.edu/ec-p/data/wooldridge/jtrain
reg hrsemp grant lsales lemploy if year==1988

      Source |       SS       df       MS              Number of obs =     105
-------------+------------------------------           F(  3,   101) =   10.44
       Model |  18622.7243     3  6207.57476           Prob > F      =  0.0000
    Residual |  60031.0957   101  594.367284           R-squared     =  0.2368
-------------+------------------------------           Adj R-squared =  0.2141
       Total |    78653.82   104  756.286731           Root MSE      =   24.38

------------------------------------------------------------------------------
      hrsemp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       grant |    26.2545   5.591766     4.70   0.000     15.16194    37.34706
      lsales |  -.9845776   3.539904    -0.28   0.781    -8.006795     6.03764
     lemploy |  -6.069873   3.882894    -1.56   0.121    -13.77249    1.632744
       _cons |   46.66504   43.41211     1.07   0.285     -39.4529     132.783
------------------------------------------------------------------------------

Example 7.4: Housing Price Regression

use http://fmwww.bc.edu/ec-p/data/wooldridge/hprice1
reg lprice llotsize lsqrft bdrms colonial

      Source |       SS       df       MS              Number of obs =      88
-------------+------------------------------           F(  4,    83) =   38.38
       Model |  5.20400088     4  1.30100022           Prob > F      =  0.0000
    Residual |  2.81362108    83  .033899049           R-squared     =  0.6491
-------------+------------------------------           Adj R-squared =  0.6322
       Total |  8.01762195    87  .092156574           Root MSE      =  .18412

------------------------------------------------------------------------------
      lprice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    llotsize |   .1678202   .0381806     4.40   0.000     .0918805    .2437599
      lsqrft |   .7071932   .0928019     7.62   0.000     .5226139    .8917725
       bdrms |   .0268308   .0287235     0.93   0.353    -.0302992    .0839608
    colonial |   .0537949   .0447732     1.20   0.233    -.0352572     .142847
       _cons |   5.558154   .6510406     8.54   0.000     4.263261    6.853048
------------------------------------------------------------------------------

Example 7.5: Log Hourly Wage Equation

use http://fmwww.bc.edu/ec-p/data/wooldridge/wage1
reg lwage female educ exper expersq tenure tenursq

      Source |       SS       df       MS              Number of obs =     526
-------------+------------------------------           F(  6,   519) =   68.18
       Model |  65.3791002     6  10.8965167           Prob > F      =  0.0000
    Residual |  82.9506616   519  .159827864           R-squared     =  0.4408
-------------+------------------------------           Adj R-squared =  0.4343
       Total |  148.329762   525   .28253288           Root MSE      =  .39978

------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      female |   -.296511   .0358055    -8.28   0.000    -.3668524   -.2261695
        educ |   .0801967   .0067573    11.87   0.000     .0669217    .0934716
       exper |   .0294324   .0049752     5.92   0.000     .0196584    .0392063
     expersq |  -.0005827   .0001073    -5.43   0.000    -.0007935   -.0003719
      tenure |   .0317139   .0068452     4.63   0.000     .0182663    .0451616
     tenursq |  -.0005852   .0002347    -2.49   0.013    -.0010463   -.0001241
       _cons |   .4166909   .0989279     4.21   0.000     .2223425    .6110393
------------------------------------------------------------------------------

Difference between woman's and man's wage

di exp(_b[female]*1)-1
-.25659254

Example 7.6: Log Hourly Wage Equation

use http://fmwww.bc.edu/ec-p/data/wooldridge/wage1
gen male = (!female)
gen single = (~married)
gen marrmale = (married & ~female)
gen marrfem = (married & female)
gen singfem = (female & ~married)
gen singmale = (~female & ~married)
reg lwage marrmale marrfem singfem educ exper expersq tenure tenursq

      Source |       SS       df       MS              Number of obs =     526
-------------+------------------------------           F(  8,   517) =   55.25
       Model |  68.3617614     8  8.54522017           Prob > F      =  0.0000
    Residual |  79.9680004   517  .154676983           R-squared     =  0.4609
-------------+------------------------------           Adj R-squared =  0.4525
       Total |  148.329762   525   .28253288           Root MSE      =  .39329

------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    marrmale |   .2126756   .0553572     3.84   0.000      .103923    .3214283
     marrfem |  -.1982676   .0578355    -3.43   0.001    -.3118891   -.0846462
     singfem |  -.1103502   .0557421    -1.98   0.048     -.219859   -.0008414
        educ |   .0789103   .0066945    11.79   0.000     .0657585    .0920621
       exper |   .0268006   .0052428     5.11   0.000     .0165007    .0371005
     expersq |  -.0005352   .0001104    -4.85   0.000    -.0007522   -.0003183
      tenure |   .0290875    .006762     4.30   0.000     .0158031    .0423719
     tenursq |  -.0005331   .0002312    -2.31   0.022    -.0009874   -.0000789
       _cons |    .321378    .100009     3.21   0.001     .1249041     .517852
------------------------------------------------------------------------------

Difference in lwage between married and single women

lincom singfem-marrfem

------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0879174   .0523481     1.68   0.094    -.0149238    .1907587
------------------------------------------------------------------------------
reg lwage marrmale singmale singfem educ exper expersq tenure tenursq

      Source |       SS       df       MS              Number of obs =     526
-------------+------------------------------           F(  8,   517) =   55.25
       Model |  68.3617614     8  8.54522017           Prob > F      =  0.0000
    Residual |  79.9680004   517  .154676983           R-squared     =  0.4609
-------------+------------------------------           Adj R-squared =  0.4525
       Total |  148.329762   525   .28253288           Root MSE      =  .39329

------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    marrmale |   .4109433   .0457709     8.98   0.000     .3210234    .5008631
    singmale |   .1982676   .0578355     3.43   0.001     .0846462    .3118891
     singfem |   .0879174   .0523481     1.68   0.094    -.0149238    .1907587
        educ |   .0789103   .0066945    11.79   0.000     .0657585    .0920621
       exper |   .0268006   .0052428     5.11   0.000     .0165007    .0371005
     expersq |  -.0005352   .0001104    -4.85   0.000    -.0007522   -.0003183
      tenure |   .0290875    .006762     4.30   0.000     .0158031    .0423719
     tenursq |  -.0005331   .0002312    -2.31   0.022    -.0009874   -.0000789
       _cons |   .1231104   .1057937     1.16   0.245     -.084728    .3309488
------------------------------------------------------------------------------

Example 7.7: Effects of Physical Attractiveness on Wage

Dataset is not available

Example 7.8: Effects of Law School Rankings on Starting Salaries

use http://fmwww.bc.edu/ec-p/data/wooldridge/lawsch85
gen r61_100 = (rank>60 & rank<101)
reg lsalary top10 r11_25 r26_40 r41_60 r61_100 LSAT GPA llibvol lcost

      Source |       SS       df       MS              Number of obs =     136
-------------+------------------------------           F(  9,   126) =  143.20
       Model |  9.45225307     9  1.05025034           Prob > F      =  0.0000
    Residual |  .924109594   126  .007334203           R-squared     =  0.9109
-------------+------------------------------           Adj R-squared =  0.9046
       Total |  10.3763627   135  .076861946           Root MSE      =  .08564

------------------------------------------------------------------------------
     lsalary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       top10 |   .6995646   .0534919    13.08   0.000     .5937057    .8054236
      r11_25 |   .5935444     .03944    15.05   0.000     .5154938    .6715951
      r26_40 |   .3750779   .0340812    11.01   0.000     .3076322    .4425236
      r41_60 |     .26282    .027962     9.40   0.000     .2074839    .3181561
     r61_100 |   .1315946   .0210418     6.25   0.000     .0899535    .1732358
        LSAT |   .0056908    .003063     1.86   0.066    -.0003708    .0117524
         GPA |   .0137274   .0741919     0.19   0.854    -.1330962    .1605509
     llibvol |   .0363614   .0260165     1.40   0.165    -.0151245    .0878472
       lcost |   .0008418    .025136     0.03   0.973    -.0489017    .0505852
       _cons |   9.165292   .4114241    22.28   0.000     8.351096    9.979488
------------------------------------------------------------------------------

Difference in starting wage between top 10 below 100 school

di exp(_[top10]*1)-1
1.0137
reg lsalary rank LSAT GPA llibvol lcost

      Source |       SS       df       MS              Number of obs =     136
-------------+------------------------------           F(  5,   130) =  138.23
       Model |  8.73363382     5  1.74672676           Prob > F      =  0.0000
    Residual |  1.64272884   130  .012636376           R-squared     =  0.8417
-------------+------------------------------           Adj R-squared =  0.8356
       Total |  10.3763627   135  .076861946           Root MSE      =  .11241

------------------------------------------------------------------------------
     lsalary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        rank |  -.0033246   .0003485    -9.54   0.000     -.004014   -.0026352
        LSAT |   .0046964   .0040105     1.17   0.244    -.0032379    .0126307
         GPA |   .2475245    .090037     2.75   0.007      .069397    .4256519
     llibvol |   .0949925   .0332543     2.86   0.005     .0292028    .1607823
       lcost |   .0375543   .0321061     1.17   0.244    -.0259637    .1010723
       _cons |   8.343234   .5325191    15.67   0.000     7.289709    9.396759
------------------------------------------------------------------------------

Example 7.9: Effects of Computer Usage on Wages

Dataset is not available

Example 7.10: Log Hourly Wage Equation

use http://fmwww.bc.edu/ec-p/data/wooldridge/wage2
gen femed = female*educ
reg lwage female educ femed exper expersq tenure tenursq

      Source |       SS       df       MS              Number of obs =     526
-------------+------------------------------           F(  7,   518) =   58.37
       Model |  65.4081526     7   9.3440218           Prob > F      =  0.0000
    Residual |  82.9216091   518  .160080326           R-squared     =  0.4410
-------------+------------------------------           Adj R-squared =  0.4334
       Total |  148.329762   525   .28253288           Root MSE      =   .4001

------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      female |  -.2267887   .1675394    -1.35   0.176     -.555929    .1023516
        educ |   .0823692   .0084699     9.72   0.000     .0657296    .0990088
       femed |  -.0055645   .0130618    -0.43   0.670    -.0312252    .0200962
       exper |   .0293366   .0049842     5.89   0.000      .019545    .0391283
     expersq |  -.0005804   .0001075    -5.40   0.000    -.0007916   -.0003691
      tenure |   .0318967    .006864     4.65   0.000      .018412    .0453814
     tenursq |    -.00059   .0002352    -2.51   0.012     -.001052    -.000128
       _cons |    .388806   .1186871     3.28   0.001     .1556388    .6219733
------------------------------------------------------------------------------
reg lwage female educ exper expersq tenure tenursq

      Source |       SS       df       MS              Number of obs =     526
-------------+------------------------------           F(  6,   519) =   68.18
       Model |  65.3791002     6  10.8965167           Prob > F      =  0.0000
    Residual |  82.9506616   519  .159827864           R-squared     =  0.4408
-------------+------------------------------           Adj R-squared =  0.4343
       Total |  148.329762   525   .28253288           Root MSE      =  .39978

------------------------------------------------------------------------------
       lwage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      female |   -.296511   .0358055    -8.28   0.000    -.3668524   -.2261695
        educ |   .0801967   .0067573    11.87   0.000     .0669217    .0934716
       exper |   .0294324   .0049752     5.92   0.000     .0196584    .0392063
     expersq |  -.0005827   .0001073    -5.43   0.000    -.0007935   -.0003719
      tenure |   .0317139   .0068452     4.63   0.000     .0182663    .0451616
     tenursq |  -.0005852   .0002347    -2.49   0.013    -.0010463   -.0001241
       _cons |   .4166909   .0989279     4.21   0.000     .2223425    .6110393
------------------------------------------------------------------------------

Example 7.11: Effects of Race on Baseball Player Salaries

use http://fmwww.bc.edu/ec-p/data/wooldridge/mlb1
reg lsalary years gamesyr bavg hrunsyr rbisyr runsyr fldperc allstar black hispan blckpb hispph

      Source |       SS       df       MS              Number of obs =     330
-------------+------------------------------           F( 12,   317) =   46.48
       Model |  283.782211    12  23.6485176           Prob > F      =  0.0000
    Residual |  161.279291   317   .50876748           R-squared     =  0.6376
-------------+------------------------------           Adj R-squared =  0.6239
       Total |  445.061503   329  1.35277053           Root MSE      =  .71328

------------------------------------------------------------------------------
     lsalary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       years |   .0673458   .0128915     5.22   0.000     .0419821    .0927094
     gamesyr |   .0088778   .0033837     2.62   0.009     .0022205    .0155352
        bavg |   .0009451   .0015133     0.62   0.533    -.0020322    .0039225
     hrunsyr |   .0146206   .0164522     0.89   0.375    -.0177488      .04699
      rbisyr |   .0044938    .007575     0.59   0.553    -.0104098    .0193974
      runsyr |   .0072029   .0045671     1.58   0.116    -.0017827    .0161884
     fldperc |   .0010865   .0021195     0.51   0.609    -.0030836    .0052566
     allstar |   .0075307   .0028735     2.62   0.009     .0018771    .0131843
       black |  -.1980075   .1254968    -1.58   0.116    -.4449192    .0489043
      hispan |  -.1900079   .1530902    -1.24   0.215     -.491209    .1111933
      blckpb |   .0124513   .0049628     2.51   0.013     .0026871    .0222154
      hispph |   .0200862   .0097933     2.05   0.041     .0008181    .0393543
       _cons |   10.34369   2.182538     4.74   0.000       6.0496    14.63778
------------------------------------------------------------------------------

Difference in lwage between black and white in cities with 10% of blacks

lincom _b[black]+_b[blckpb]*10

 ( 1)  black + 10.0 blckpb = 0.0

------------------------------------------------------------------------------
     lsalary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0734949   .0997916    -0.74   0.462    -.2698324    .1228426
------------------------------------------------------------------------------

Difference in lwage between black and white in cities with 20% of blacks

lincom _b[black]+_b[blckpb]*20

 ( 1)  black + 20.0 blckpb = 0.0

------------------------------------------------------------------------------
     lsalary |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0510177   .0953577     0.54   0.593    -.1365962    .2386316
------------------------------------------------------------------------------

City percentage of hispanic people when wages of hispanic and whites are equal

di _b[hispan]*-1/_b[hispph]
9.4596276

Example 7.12: A Linear Probability Model of Arrests

use http://fmwww.bc.edu/ec-p/data/wooldridge/crime1
gen arr86=(~narr86)
reg arr86 pcnv avgsen tottime ptime86 qemp86

      Source |       SS       df       MS              Number of obs =    2725
-------------+------------------------------           F(  5,  2719) =   27.03
       Model |  25.8452455     5  5.16904909           Prob > F      =  0.0000
    Residual |  519.971268  2719  .191236215           R-squared     =  0.0474
-------------+------------------------------           Adj R-squared =  0.0456
       Total |  545.816514  2724   .20037317           Root MSE      =  .43731

------------------------------------------------------------------------------
       arr86 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        pcnv |   .1624448   .0212368     7.65   0.000      .120803    .2040866
      avgsen |  -.0061127    .006452    -0.95   0.344     -.018764    .0065385
     tottime |   .0022616   .0049781     0.45   0.650    -.0074997    .0120229
     ptime86 |   .0219664   .0046349     4.74   0.000     .0128781    .0310547
      qemp86 |   .0428294   .0054046     7.92   0.000     .0322319    .0534268
       _cons |   .5593846   .0172329    32.46   0.000     .5255937    .5931754
------------------------------------------------------------------------------

Change in probability of arrest if pcnv increases by .5

lincom _b[pcnv]*.5

 ( 1)  .5 pcnv = 0.0

------------------------------------------------------------------------------
       arr86 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0812224   .0106184     7.65   0.000     .0604015    .1020433
------------------------------------------------------------------------------

Change in probability of arrest if ptime86 increases by 6

lincom _b[ptime86]*6

 ( 1)  6.0 ptime86 = 0.0

------------------------------------------------------------------------------
       arr86 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .1317984   .0278095     4.74   0.000     .0772686    .1863282
------------------------------------------------------------------------------

Change in probability of arrest if ptime86 decreases by 12

lincom _b[_cons]- _b[ptime86]*12

 ( 1) - 12.0 ptime86 + _cons = 0.0

------------------------------------------------------------------------------
       arr86 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .2957878    .061983     4.77   0.000     .1742492    .4173264
------------------------------------------------------------------------------

Change in probability of arrest if qemp86 increases by 4

lincom _b[qemp86]*4

 ( 1)  4.0 qemp86 = 0.0

------------------------------------------------------------------------------
       arr86 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .1713175   .0216182     7.92   0.000     .1289277    .2137073
------------------------------------------------------------------------------
reg arr86 pcnv avgsen tottime ptime86 qemp86 black hispan

      Source |       SS       df       MS              Number of obs =    2725
-------------+------------------------------           F(  7,  2717) =   28.41
       Model |  37.2205275     7  5.31721822           Prob > F      =  0.0000
    Residual |  508.595986  2717  .187190278           R-squared     =  0.0682
-------------+------------------------------           Adj R-squared =  0.0658
       Total |  545.816514  2724   .20037317           Root MSE      =  .43265

------------------------------------------------------------------------------
       arr86 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        pcnv |    .152062   .0210655     7.22   0.000     .1107561     .193368
      avgsen |  -.0046191   .0063888    -0.72   0.470    -.0171465    .0079083
     tottime |   .0025619   .0049259     0.52   0.603    -.0070969    .0122207
     ptime86 |   .0236954   .0045948     5.16   0.000     .0146858     .032705
      qemp86 |   .0384737   .0054016     7.12   0.000     .0278821    .0490653
       black |  -.1697631   .0236738    -7.17   0.000    -.2161836   -.1233426
      hispan |  -.0961866   .0207105    -4.64   0.000    -.1367965   -.0555766
       _cons |   .6195717   .0187272    33.08   0.000     .5828507    .6562927
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This page prepared by Oleksandr Talavera (revised 8 Nov 2002)

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