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  • #16
    Marry:
    why not posting what you typed and what Stata gave you back (as per FAQ)? Thanks.
    Why creating interactions by hand when -fvvarlist- notation is available?
    Kind regards,
    Carlo
    (Stata 19.0)

    Comment


    • #17
      Thank you Carlo Lazzaro

      I typed this to test the parallel assumption for using DID approach, where:
      Code:
      HS_quality : the outcome variable, ordered from 1 to 6, i.e., from best quality of high school to worst
      local individualchar="Male Han birthorder urban"
      local natalityBasic="parent_educ_M parent_educ_F ageM_atbirth"
      local natalityUnrestricted="ageF_atbirth neverS_F elemS_F middS_F seconS_F uni_F neverS_M elemS_M middS_M seconS_M uni_M"
      local weather="PRCP PRCP2 PRCP3 maxtemp3339 maxtemp4049 maxtemp5059 maxtemp6069 maxtemp7079 maxtemp8089  maxtemp90"
      local provinceXyearFE="i.provinceXyear"
      
      Policy: a dummy variable that=1 if a public policy is applied in that county in 1998
      i.coun: for county fixed effects
      Code:
      ologit HS_quality  c.Policy##ib1997.year_birth `individualchar'  `natalityBasic' `weather' `natalityUnrestricted' i.coun `provinceXyearFE' , cluster(coun)
      What Stata gave:
      Code:
      note: uni_F omitted because of collinearity
      note: uni_M omitted because of collinearity
      note: 430602.coun omitted because of collinearity
      note: 371999.provinceXyear omitted because of collinearity
      note: 411999.provinceXyear omitted because of collinearity
      note: 431999.provinceXyear omitted because of collinearity
      note: 441999.provinceXyear omitted because of collinearity
      note: 621992.provinceXyear omitted because of collinearity
      note: 621993.provinceXyear omitted because of collinearity
      note: 621994.provinceXyear omitted because of collinearity
      note: 621995.provinceXyear omitted because of collinearity
      note: 621996.provinceXyear omitted because of collinearity
      note: 621997.provinceXyear omitted because of collinearity
      note: 621998.provinceXyear omitted because of collinearity
      note: 621999.provinceXyear omitted because of collinearity
      Iteration 0:   log pseudolikelihood = -343.06255  
      Iteration 1:   log pseudolikelihood = -276.04731  
      Iteration 2:   log pseudolikelihood = -270.44204  
      Iteration 3:   log pseudolikelihood = -266.30987  
      Iteration 4:   log pseudolikelihood = -266.23125  
      Iteration 5:   log pseudolikelihood = -266.23116  
      Iteration 6:   log pseudolikelihood = -266.23116  
      
      Ordered logistic regression                     Number of obs     =        240
                                                      Wald chi2(7)      =          .
                                                      Prob > chi2       =          .
      Log pseudolikelihood = -266.23116               Pseudo R2         =     0.2240
      
                                            (Std. Err. adjusted for 10 clusters in coun)
      ----------------------------------------------------------------------------------
                       |               Robust
            HS_quality |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
      -----------------+----------------------------------------------------------------
                   Policy |   52.48485   164.7722     0.32   0.750    -270.4628    375.4325
                       |
            year_birth |
                 1992  |   3.543709   3.625405     0.98   0.328    -3.561953    10.64937
                 1993  |   1.821638   2.160974     0.84   0.399    -2.413794    6.057069
                 1994  |   .9074739   2.628122     0.35   0.730    -4.243551    6.058499
                 1995  |   1.278579   1.977988     0.65   0.518    -2.598206    5.155364
                 1996  |  -1.760121   1.748809    -1.01   0.314    -5.187723    1.667481
                 1998  |  -1.638686   2.479953    -0.66   0.509    -6.499304    3.221932
                 1999  |   1.214632   4.755089     0.26   0.798     -8.10517    10.53443
                       |
      year_birth#c.Policy|
                 1992  |   .9987586   4.050904     0.25   0.805    -6.940868    8.938385
                 1993  |    1.44782    2.89902     0.50   0.617    -4.234155    7.129796
                 1994  |   .9700499   2.170729     0.45   0.655    -3.284502    5.224601
                 1995  |   .0578328   1.762561     0.03   0.974    -3.396723    3.512388
                 1996  |  -.6788812   4.642087    -0.15   0.884    -9.777204    8.419442
                 1998  |   5.829671   6.609967     0.88   0.378    -7.125626    18.78497
                 1999  |  -1.845757   6.243948    -0.30   0.768    -14.08367    10.39216
                       |
                 Malem |   .2914989   .5702782     0.51   0.609    -.8262258    1.409224
                   Han |   .7020968   1.334465     0.53   0.599    -1.913407    3.317601
            birthorder |   .4668223    .402836     1.16   0.247    -.3227218    1.256366
                 urban |  -.1860817   .5850726    -0.32   0.750    -1.332803    .9606395
         parent_educ_M |   .2432578   .2758895     0.88   0.378    -.2974756    .7839912
         parent_educ_F |  -.0291394    .338625    -0.09   0.931    -.6928323    .6345534
          ageM_atbirth |  -.0875154   .0690289    -1.27   0.205    -.2228097    .0477788
                  PRCP |  -.0785405   .1764216    -0.45   0.656    -.4243205    .2672395
                 PRCP2 |   .0001179   .0003002     0.39   0.695    -.0004704    .0007062
                 PRCP3 |  -5.58e-08   1.58e-07    -0.35   0.724    -3.66e-07    2.54e-07
           maxtemp3339 |   .2145753   .2504104     0.86   0.392    -.2762202    .7053707
           maxtemp4049 |   .1020753   .1748543     0.58   0.559    -.2406328    .4447835
           maxtemp5059 |  -.0529533   .2612522    -0.20   0.839    -.5649981    .4590916
           maxtemp6069 |  -.2306455   .2838317    -0.81   0.416    -.7869453    .3256544
           maxtemp7079 |    .070843   .1938286     0.37   0.715    -.3090541    .4507401
           maxtemp8089 |   .3152509   .3126828     1.01   0.313    -.2975961    .9280979
             maxtemp90 |   .0246777   .0427276     0.58   0.564    -.0590668    .1084222
          ageF_atbirth |   .1046381   .0699428     1.50   0.135    -.0324472    .2417235
              neverS_F |   .2455865   4.423103     0.06   0.956    -8.423537     8.91471
               elemS_F |   .0754691   2.711112     0.03   0.978    -5.238213    5.389151
               middS_F |   .1825994   1.473938     0.12   0.901    -2.706265    3.071464
              seconS_F |  -.4423052   .7581791    -0.58   0.560    -1.928309    1.043698
                 uni_F |          0  (omitted)
              neverS_M |   4.222651   2.540211     1.66   0.096    -.7560721    9.201373
               elemS_M |   3.727541   2.274819     1.64   0.101    -.7310221    8.186104
               middS_M |   3.428071   1.469168     2.33   0.020     .5485542    6.307588
              seconS_M |   2.081799   1.097176     1.90   0.058    -.0686269    4.232226
                 uni_M |          0  (omitted)
                       |
                  coun |
               370285  |  -49.08833   164.6321    -0.30   0.766    -371.7613    273.5846
               370522  |   2.287403   7.714629     0.30   0.767    -12.83299     17.4078
               371521  |   1.423363   7.484052     0.19   0.849    -13.24511    16.09183
               419001  |    -53.408   166.4635    -0.32   0.748    -379.6705    272.8545
               430602  |          0  (omitted)
               440903  |   13.19228   67.13979     0.20   0.844    -118.3993    144.7839
               620502  |  -.6549835   3.207595    -0.20   0.838    -6.941755    5.631787
               620522  |   1.443846   3.408562     0.42   0.672    -5.236812    8.124504
               621022  |  -.0399038   2.816934    -0.01   0.989    -5.560993    5.481185
                       |
         provinceXyear |
               111993  |  -.2139994   1.714582    -0.12   0.901    -3.574519     3.14652
               111994  |   1.632921    2.92698     0.56   0.577    -4.103855    7.369697
               111995  |    3.79042   1.997047     1.90   0.058    -.1237204    7.704561
               111996  |   2.735254   3.159176     0.87   0.387    -3.456617    8.927125
               111997  |   5.323311   2.690759     1.98   0.048     .0495206     10.5971
               111998  |   4.324856   2.377266     1.82   0.069    -.3344989    8.984211
               111999  |  -3.764474   6.661268    -0.57   0.572    -16.82032    9.291371
               371992  |  -3.921838    7.14803    -0.55   0.583    -17.93172    10.08804
               371993  |  -6.260062   8.984909    -0.70   0.486    -23.87016    11.35004
               371994  |  -2.989583   6.179958    -0.48   0.629    -15.10208    9.122913
               371995  |  -3.052529    8.14935    -0.37   0.708    -19.02496     12.9199
               371996  |   .1620652   8.032402     0.02   0.984    -15.58115    15.90528
               371997  |    -3.3196   6.663181    -0.50   0.618    -16.37919    9.739994
               371998  |  -1.993721   6.526919    -0.31   0.760    -14.78625    10.79881
               371999  |          0  (omitted)
               411992  |  -1.488893   6.597484    -0.23   0.821    -14.41972    11.44194
               411993  |   1.881748   7.023569     0.27   0.789    -11.88419    15.64769
               411994  |   3.143834   6.551835     0.48   0.631    -9.697526    15.98519
               411995  |   .7399968   6.378721     0.12   0.908    -11.76207    13.24206
               411996  |   6.820483   9.477719     0.72   0.472     -11.7555    25.39647
               411997  |   3.634315   6.803934     0.53   0.593    -9.701151    16.96978
               411998  |  -34.45104   7.868837    -4.38   0.000    -49.87368    -19.0284
               411999  |          0  (omitted)
               431992  |  -40.97416   105.0452    -0.39   0.696     -246.859    164.9107
               431993  |  -8.756741   7.458386    -1.17   0.240    -23.37491    5.861427
               431994  |  -7.218318   9.316863    -0.77   0.438    -25.47903     11.0424
               431995  |  -29.59436   71.34127    -0.41   0.678    -169.4207     110.232
               431996  |  -67.00535   108.4198    -0.62   0.537    -279.5042    145.4935
               431997  |   21.91589   86.89899     0.25   0.801     -148.403    192.2348
               431998  |   22.29626   79.99357     0.28   0.780    -134.4883    179.0808
               431999  |          0  (omitted)
               441992  |   20.40724   51.21338     0.40   0.690    -79.96915    120.7836
               441993  |  -.6846402   23.77471    -0.03   0.977    -47.28222    45.91294
               441994  |   77.38496   222.3357     0.35   0.728     -358.385    513.1549
               441995  |   13.05128   28.30204     0.46   0.645     -42.4197    68.52227
               441996  |  -8.789285    55.6824    -0.16   0.875    -117.9248    100.3462
               441997  |    65.6199    209.916     0.31   0.755    -345.8078    477.0476
               441998  |   2.518779   4.121442     0.61   0.541    -5.559098    10.59666
               441999  |          0  (omitted)
               621992  |          0  (omitted)
               621993  |          0  (omitted)
               621994  |          0  (omitted)
               621995  |          0  (omitted)
               621996  |          0  (omitted)
               621997  |          0  (omitted)
               621998  |          0  (omitted)
               621999  |          0  (omitted)
      -----------------+----------------------------------------------------------------
                 /cut1 |   -3.15688   45.60928                     -92.54943    86.23567
                 /cut2 |  -1.045141   45.31735                     -89.86552    87.77524
                 /cut3 |   1.856915   45.27024                     -86.87113    90.58496
                 /cut4 |   4.367487    45.5195                     -84.84909    93.58407
                 /cut5 |   6.749063   45.79089                     -82.99944    96.49756
      ----------------------------------------------------------------------------------
      Note: 3 observations completely determined.  Standard errors questionable.
      
      .
      Then I run the DID regression where:
      Code:
      post_Policy: a dummy variable that =1 if year of birth>=1998
      Code:
      ologit HS_quality  i.Policy##i.post_Policy `individualchar'  `natalityBasic' `weather' `natalityUnrestricted' i.coun `provinceXyearFE' , cluster(coun)
      This is what Stata gave:
      Code:
      note: uni_F omitted because of collinearity
      note: uni_M omitted because of collinearity
      note: 430602.coun omitted because of collinearity
      note: 371999.provinceXyear omitted because of collinearity
      note: 411999.provinceXyear omitted because of collinearity
      note: 431999.provinceXyear omitted because of collinearity
      note: 441999.provinceXyear omitted because of collinearity
      note: 621997.provinceXyear omitted because of collinearity
      note: 621999.provinceXyear omitted because of collinearity
      Iteration 0:   log pseudolikelihood = -343.06255  
      Iteration 1:   log pseudolikelihood = -277.85543  
      Iteration 2:   log pseudolikelihood = -272.63457  
      Iteration 3:   log pseudolikelihood =  -268.4858  
      Iteration 4:   log pseudolikelihood = -268.40876  
      Iteration 5:   log pseudolikelihood = -268.40868  
      Iteration 6:   log pseudolikelihood = -268.40868  
      
      Ordered logistic regression                     Number of obs     =        240
                                                      Wald chi2(8)      =          .
                                                      Prob > chi2       =          .
      Log pseudolikelihood = -268.40868               Pseudo R2         =     0.2176
      
                                              (Std. Err. adjusted for 10 clusters in coun)
      ------------------------------------------------------------------------------------
                         |               Robust
              HS_quality |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
      -------------------+----------------------------------------------------------------
                   1.Policy|  -3.325627   213.5136    -0.02   0.988    -421.8046    415.1533
        1.post_Policy |   1.114469   4.863336     0.23   0.819    -8.417494    10.64643
                         |
      TCZ#post_Policy |
                    1 1  |   .9683091   5.224791     0.19   0.853    -9.272093    11.20871
                         |
                   Malem |   .2987034   .5276351     0.57   0.571    -.7354425    1.332849
                     Han |   .8139503   1.278432     0.64   0.524    -1.691731    3.319631
              birthorder |   .5031019   .3951382     1.27   0.203    -.2713547    1.277558
                   urban |  -.2367969   .5584532    -0.42   0.672    -1.331345    .8577513
           parent_educ_M |   .2061715   .2551651     0.81   0.419    -.2939429    .7062859
           parent_educ_F |   .0011697   .3376632     0.00   0.997    -.6606379    .6629774
            ageM_atbirth |   -.084713   .0703375    -1.20   0.228     -.222572    .0531461
                    PRCP |   .0065246   .2130412     0.03   0.976    -.4110285    .4240778
                   PRCP2 |  -.0000166   .0003626    -0.05   0.964    -.0007272    .0006941
                   PRCP3 |   8.06e-09   1.95e-07     0.04   0.967    -3.74e-07    3.90e-07
             maxtemp3339 |   .1870965   .1048865     1.78   0.074    -.0184772    .3926703
             maxtemp4049 |   .0479297   .0685725     0.70   0.485    -.0864699    .1823294
             maxtemp5059 |  -.0375316   .2075456    -0.18   0.856    -.4443135    .3692504
             maxtemp6069 |  -.1349889   .3905689    -0.35   0.730    -.9004898     .630512
             maxtemp7079 |   .0783595   .1905878     0.41   0.681    -.2951857    .4519047
             maxtemp8089 |   .2569244     .15709     1.64   0.102    -.0509663    .5648152
               maxtemp90 |   .0271747    .048479     0.56   0.575    -.0678424    .1221919
            ageF_atbirth |    .104015   .0660542     1.57   0.115    -.0254488    .2334788
                neverS_F |   .7239396   4.417896     0.16   0.870    -7.934977    9.382857
                 elemS_F |   .3855843   2.801629     0.14   0.891    -5.105507    5.876676
                 middS_F |   .4455614   1.576606     0.28   0.777    -2.644529    3.535652
                seconS_F |  -.3011863   .9002103    -0.33   0.738    -2.065566    1.463193
                   uni_F |          0  (omitted)
                neverS_M |   3.544326   2.300968     1.54   0.123    -.9654893    8.054141
                 elemS_M |   3.307752   2.138379     1.55   0.122    -.8833932    7.498897
                 middS_M |   3.131184    1.42029     2.20   0.027     .3474666    5.914901
                seconS_M |    1.84237   1.091482     1.69   0.091    -.2968961    3.981636
                   uni_M |          0  (omitted)
                         |
                    coun |
                 370285  |   2.002268   218.5641     0.01   0.993    -426.3755      430.38
                 370522  |  -3.063214   6.163929    -0.50   0.619    -15.14429    9.017865
                 371521  |  -3.453954    5.75794    -0.60   0.549    -14.73931      7.8314
                 419001  |  -2.966447   220.9121    -0.01   0.989    -435.9462    430.0133
                 430602  |          0  (omitted)
                 440903  |  -6.855274   89.79573    -0.08   0.939    -182.8517    169.1411
                 620502  |  -3.197909   3.474462    -0.92   0.357    -10.00773    3.611912
                 620522  |  -1.118763   3.403608    -0.33   0.742    -7.789712    5.552186
                 621022  |  -2.526687   4.027321    -0.63   0.530    -10.42009    5.366717
                         |
           provinceXyear |
                 111993  |  -1.618043   2.717033    -0.60   0.551     -6.94333    3.707244
                 111994  |   1.217135   1.957192     0.62   0.534     -2.61889    5.053161
                 111995  |   1.736707   2.874914     0.60   0.546    -3.898022    7.371436
                 111996  |  -.2979835   6.046278    -0.05   0.961    -12.14847     11.5525
                 111997  |   1.972686   4.972875     0.40   0.692    -7.773969    11.71934
                 111998  |    .099168   5.278918     0.02   0.985    -10.24732    10.44566
                 111999  |  -4.523318   7.411652    -0.61   0.542    -19.04989    10.00325
                 371992  |   1.407122   5.166483     0.27   0.785    -8.718999    11.53324
                 371993  |   .2406475   5.992786     0.04   0.968      -11.505    11.98629
                 371994  |   .9302862   5.015421     0.19   0.853    -8.899758    10.76033
                 371995  |   .9361114   5.579907     0.17   0.867    -10.00031    11.87253
                 371996  |   .4190308   5.043476     0.08   0.934    -9.466001    10.30406
                 371997  |  -1.364449   4.912515    -0.28   0.781     -10.9928    8.263904
                 371998  |    .125294    3.02427     0.04   0.967    -5.802167    6.052755
                 371999  |          0  (omitted)
                 411992  |   6.198911   6.447591     0.96   0.336    -6.438136    18.83596
                 411993  |   6.908347    7.39861     0.93   0.350    -7.592662    21.40936
                 411994  |   8.256418   5.743579     1.44   0.151     -3.00079    19.51363
                 411995  |   4.170102   6.601126     0.63   0.528    -8.767867    17.10807
                 411996  |   6.421657   7.555759     0.85   0.395    -8.387359    21.23067
                 411997  |   5.530455    7.16519     0.77   0.440     -8.51306    19.57397
                 411998  |  -27.72484    2.06775   -13.41   0.000    -31.77756   -23.67213
                 411999  |          0  (omitted)
                 431992  |   1.416075   129.2093     0.01   0.991    -251.8296    254.6617
                 431993  |  -3.883329   9.607206    -0.40   0.686    -22.71311    14.94645
                 431994  |   1.408788   10.19479     0.14   0.890    -18.57263    21.39021
                 431995  |  -.4800286   89.18355    -0.01   0.996    -175.2766    174.3165
                 431996  |  -30.62023   135.1576    -0.23   0.821    -295.5243    234.2838
                 431997  |  -4.101913    108.901    -0.04   0.970    -217.5439    209.3401
                 431998  |  -2.139216   106.7631    -0.02   0.984     -211.391    207.1126
                 431999  |          0  (omitted)
                 441992  |   5.710464   69.59835     0.08   0.935    -130.6998    142.1207
                 441993  |   7.967308   29.37319     0.27   0.786    -49.60308     65.5377
                 441994  |   1.114347   284.6416     0.00   0.997    -556.7729    559.0016
                 441995  |   4.217804   38.50055     0.11   0.913     -71.2419     79.6775
                 441996  |  -29.19495   72.15402    -0.40   0.686    -170.6142    112.2243
                 441997  |  -6.545837   267.5452    -0.02   0.980    -530.9248    517.8331
                 441998  |   .6049397   3.137401     0.19   0.847    -5.544253    6.754132
                 441999  |          0  (omitted)
                 621992  |   2.095409   4.707817     0.45   0.656    -7.131743    11.32256
                 621993  |   1.030783   2.395518     0.43   0.667    -3.664346    5.725913
                 621994  |   .4504494   2.574795     0.17   0.861    -4.596056    5.496955
                 621995  |   .4973247   2.218462     0.22   0.823    -3.850781     4.84543
                 621996  |  -.9591419   .9569497    -1.00   0.316    -2.834729    .9164451
                 621997  |          0  (omitted)
                 621998  |  -2.783219   3.355281    -0.83   0.407    -9.359448     3.79301
                 621999  |          0  (omitted)
      -------------------+----------------------------------------------------------------
                   /cut1 |   11.23003   60.34749                     -107.0489    129.5089
                   /cut2 |   13.33331   60.04551                     -104.3537    131.0203
                   /cut3 |   16.18786   60.12679                     -101.6585    134.0342
                   /cut4 |   18.67733   60.27278                     -99.45515    136.8098
                   /cut5 |   21.05752   60.52264                     -97.56468    139.6797
      ------------------------------------------------------------------------------------
      Note: 3 observations completely determined.  Standard errors questionable.
      Last edited by Marry Lee; 25 Nov 2020, 11:16.

      Comment


      • #18
        Marry:
        you have many very high p-value, that I intepret a sign of an excess in predictors.
        Can't you consider a more parsimonious model?
        Please also note that the results of model with so many regressors are difficult to explain/disseminate.
        Kind regards,
        Carlo
        (Stata 19.0)

        Comment


        • #19
          Carlo Lazzaro Really thank you for all your remarks.
          I can do that, but if I receive questions later, like why don't you include such a variable?
          Can I just answer: because the sample size does not allow for a good estimation with a large number of variables?

          Comment


          • #20
            Marry:
            the main issue is whether or not your model gives a fair and true view of the data generating process (I do not think it does, as too may sky-rocketing p-values appear in the outcome table).
            Skim through the literature of your research field and see what others did when dealing with your very same research topic.
            Kind regards,
            Carlo
            (Stata 19.0)

            Comment


            • #21
              I will do that.
              Thank you Carlo Lazzaro for your patience and your help.
              Best Regards,
              Marry

              Comment


              • #22
                I have run a probit regression in stata. I am getting the results, but the 'completely determined' note is also showing up below the regression - 2 failures and 0 successes completely determined. Can someone please help me out?

                Code:
                . probit govt logtuiexp male rural logfeediff mpce i.statecode [pweight=Multiplier_combined], vce(cluster statedist)
                
                Iteration 0:   log pseudolikelihood =  -25360737  
                Iteration 1:   log pseudolikelihood =  -21077326  
                Iteration 2:   log pseudolikelihood =  -20998523  
                Iteration 3:   log pseudolikelihood =  -20998111  
                Iteration 4:   log pseudolikelihood =  -20998111  
                
                Probit regression                                 Number of obs   =      19824
                                                                  Wald chi2(26)   =          .
                                                                  Prob > chi2     =          .
                Log pseudolikelihood =  -20998111                 Pseudo R2       =     0.1720
                
                                            (Std. Err. adjusted for 459 clusters in statedist)
                ------------------------------------------------------------------------------
                             |               Robust
                        govt |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
                -------------+----------------------------------------------------------------
                   logtuiexp |   -.087014   .0512987    -1.70   0.090    -.1875576    .0135296
                        male |  -.0575367   .0348852    -1.65   0.099    -.1259104     .010837
                       rural |   .4264109    .052248     8.16   0.000     .3240066    .5288151
                  logfeediff |    .260134   .1616949     1.61   0.108     -.056782    .5770501
                        mpce |  -.0001794   .0000195    -9.19   0.000    -.0002177   -.0001411
                             |
                   statecode |
                         PB  |   .0842535   .1245063     0.68   0.499    -.1597744    .3282813
                         UK  |   .4422519   .1691147     2.62   0.009     .1107932    .7737105
                         HR  |  -.3501435   .1540731    -2.27   0.023    -.6521212   -.0481657
                         DL  |   .6645826   .1317916     5.04   0.000     .4062758    .9228893
                         RJ  |  -.3057149   .1402579    -2.18   0.029    -.5806154   -.0308145
                         UP  |  -.3644674    .137062    -2.66   0.008    -.6331041   -.0958307
                         BH  |   .7646985   .1461733     5.23   0.000     .4782041    1.051193
                         AR  |    1.31337   .2251185     5.83   0.000     .8721463    1.754595
                         TR  |    1.38797   .0994427    13.96   0.000     1.193066    1.582875
                         AS  |   .7507328   .1449467     5.18   0.000     .4666426    1.034823
                         WB  |   1.214339   .1736075     6.99   0.000     .8740743    1.554603
                         JH  |   .5650113   .1830502     3.09   0.002     .2062394    .9237832
                         OR  |    .629077   .1487104     4.23   0.000       .33761     .920544
                         CH  |   .5204726   .1705492     3.05   0.002     .1862023    .8547428
                         MP  |   .0842684   .1506843     0.56   0.576    -.2110675    .3796042
                         GJ  |    .548019   .1866157     2.94   0.003      .182259     .913779
                         MH  |   .7811124   .1325667     5.89   0.000     .5212863    1.040938
                         AP  |  -.9018068   .1606481    -5.61   0.000    -1.216671   -.5869424
                         KA  |   .3190452   .1417833     2.25   0.024     .0411551    .5969354
                         KR  |   .5511887   .1320787     4.17   0.000     .2923192    .8100582
                         TN  |   .1013269   .1744149     0.58   0.561      -.24052    .4431739
                         TG  |  -.8316177   .3244032    -2.56   0.010    -1.467436   -.1957991
                             |
                       _cons |  -.9193107   1.355321    -0.68   0.498    -3.575691     1.73707
                ------------------------------------------------------------------------------
                Note: 2 failures and 0 successes completely determined.

                Comment

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