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

      name:  <unnamed>

       log:  C:\Users\mexmi\Documents\newer web pages\Soc_382\logs\3rd loglin intermar class.log

  log type:  text

 opened on:   5 Feb 2019, 10:16:22

 

. use "C:\Users\mexmi\Documents\newer web pages\Soc_382\Treimans ch 12 occ mobility data 6x6 versi

> on with extra vars.dta", clear

 

. clear all

 

. use "C:\Users\mexmi\Documents\newer web pages\Soc_382\five cat intermar data US 3 decades.dta",

> clear

 

* If you go back to the beginning of intermarriage class 2 log, you will see a description of how the crossings terms were created. We have 4 crossings terms, each of which can be thought of as indicating the difficulty from crossing from group x to group x+1. So cross1 is an estimate of the likelihood of marriage between category 1 (black people) and category 2 (Mexican American people).

 

 

. poisson count i.meth_num i.feth_num i.cross*

 

Iteration 0:   log likelihood = -1628492.6 

Iteration 1:   log likelihood = -514542.18  (backed up)

Iteration 2:   log likelihood = -405521.33  (backed up)

Iteration 3:   log likelihood = -241959.01  (backed up)

Iteration 4:   log likelihood = -147645.83 

Iteration 5:   log likelihood = -59259.102 

Iteration 6:   log likelihood = -12308.206 

Iteration 7:   log likelihood = -10257.604 

Iteration 8:   log likelihood =  -10246.31 

Iteration 9:   log likelihood = -10246.309 

 

Poisson regression                                Number of obs   =         25

                                                  LR chi2(12)     = 3132470.69

                                                  Prob > chi2     =     0.0000

Log likelihood = -10246.309                       Pseudo R2       =     0.9935

 

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

              count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

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

           meth_num |

  Mexican American  |   -.977803   .0181359   -53.92   0.000    -1.013349   -.9422572

    Hispanic Other  |  -1.938943    .020284   -95.59   0.000    -1.978698   -1.899187

Non Hispanic Other  |  -2.506451   .0216409  -115.82   0.000    -2.548866   -2.464035

White non Hispanic  |   .6765227   .0185343    36.50   0.000     .6401961    .7128493

                    |

           feth_num |

  Mexican American  |  -.0199891    .018195    -1.10   0.272    -.0556507    .0156725

    Hispanic Other  |  -.8181779   .0202753   -40.35   0.000    -.8579168    -.778439

Non Hispanic Other  |  -1.107257   .0209967   -52.73   0.000     -1.14841   -1.066104

White non Hispanic  |   1.871097   .0185343   100.95   0.000      1.83477    1.907423

                    |

           1.cross1 |  -3.026666    .017803  -170.01   0.000    -3.061559   -2.991773

           1.cross2 |  -1.014698   .0101682   -99.79   0.000    -1.034627   -.9947684

           1.cross3 |  -.5411649   .0113079   -47.86   0.000     -.563328   -.5190018

           1.cross4 |  -1.444942   .0092927  -155.49   0.000    -1.463155   -1.426728

              _cons |   10.65775   .0048495  2197.69   0.000     10.64825    10.66726

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

 

. poisgof

 

         Deviance goodness-of-fit =  20262.62

         Prob > chi2(12)          =    0.0000

 

         Pearson goodness-of-fit  =  21598.27

         Prob > chi2(12)          =    0.0000

 

. * that is the crossings model. And it fits poorly, as the crossings model and the uniform association model below assume that the categories are ordinal, and these categories are not ordinal.

 

. gen score=meth_num*feth_num

 

. poisson count i.meth_num i.feth_num score

 

Iteration 0:   log likelihood = -448819.89 

Iteration 1:   log likelihood = -199150.22  (backed up)

Iteration 2:   log likelihood = -72023.171 

Iteration 3:   log likelihood = -48477.638 

Iteration 4:   log likelihood = -46237.124 

Iteration 5:   log likelihood = -46234.659 

Iteration 6:   log likelihood = -46234.659 

 

Poisson regression                                Number of obs   =         25

                                                  LR chi2(9)      = 3060493.99

                                                  Prob > chi2     =     0.0000

Log likelihood = -46234.659                       Pseudo R2       =     0.9707

 

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

              count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

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

           meth_num |

  Mexican American  |  -1.712384   .0084087  -203.64   0.000    -1.728865   -1.695903

    Hispanic Other  |   -4.42779     .01369  -323.43   0.000    -4.454622   -4.400958

Non Hispanic Other  |  -7.618328   .0212675  -358.21   0.000    -7.660012   -7.576645

White non Hispanic  |   -6.52374   .0275075  -237.16   0.000    -6.577654   -6.469826

                    |

           feth_num |

  Mexican American  |  -1.609809   .0084125  -191.36   0.000    -1.626297   -1.593321

    Hispanic Other  |  -4.125007    .012931  -319.00   0.000    -4.150352   -4.099663

Non Hispanic Other  |  -6.986868   .0190834  -366.12   0.000    -7.024271   -6.949465

White non Hispanic  |  -6.092624   .0254213  -239.67   0.000    -6.142449   -6.042799

                    |

              score |   .6448603   .0020341   317.02   0.000     .6408736    .6488471

              _cons |   9.695309   .0055428  1749.17   0.000     9.684445    9.706172

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

 

. poisgof

 

         Deviance goodness-of-fit =  92239.32

         Prob > chi2(15)          =    0.0000

 

         Pearson goodness-of-fit  =    123297

         Prob > chi2(15)          =    0.0000

 

. save "C:\Users\mexmi\Documents\newer web pages\Soc_382\five cat intermar data US 3 decades.dta",

>  replace

file C:\Users\mexmi\Documents\newer web pages\Soc_382\five cat intermar data US 3 decades.dta save

> d

 

. *that was the uniform model

 

. table meth_num feth_num, contents(mean cross1) cellwidth(10)

 

note: cellwidth too small, variable name truncated;

      to increase cellwidth, specify cellwidth(#)

 

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

husband's           |                   wife's race/ethnicity                   

race/ethnicity      | Black, non  Mexican Am  Hispanic O  Non Hispan  White non

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

Black, non Hispanic |          0           1           1           1           1

   Mexican American |          1           0           0           0           0

     Hispanic Other |          1           0           0           0           0

 Non Hispanic Other |          1           0           0           0           0

 White non Hispanic |          1           0           0           0           0

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

 

. table meth_num feth_num, contents(mean cross2) cellwidth(10)

 

note: cellwidth too small, variable name truncated;

      to increase cellwidth, specify cellwidth(#)

 

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

husband's           |                   wife's race/ethnicity                  

race/ethnicity      | Black, non  Mexican Am  Hispanic O  Non Hispan  White non

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

Black, non Hispanic |          0           0           1           1           1

   Mexican American |          0           0           1           1           1

     Hispanic Other |          1           1           0           0           0

 Non Hispanic Other |          1           1           0           0           0

 White non Hispanic |          1           1           0           0           0

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

 

. table meth_num feth_num, contents(mean cross3) cellwidth(10)

 

note: cellwidth too small, variable name truncated;

      to increase cellwidth, specify cellwidth(#)

 

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

husband's           |                   wife's race/ethnicity                  

race/ethnicity      | Black, non  Mexican Am  Hispanic O  Non Hispan  White non

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

Black, non Hispanic |          0           0           0           1           1

   Mexican American |          0           0           0           1           1

     Hispanic Other |          0           0           0           1           1

 Non Hispanic Other |          1           1           1           0           0

 White non Hispanic |          1           1           1           0           0

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

 

. table meth_num feth_num, contents(mean cross4) cellwidth(10)

 

note: cellwidth too small, variable name truncated;

      to increase cellwidth, specify cellwidth(#)

 

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

husband's           |                   wife's race/ethnicity                  

race/ethnicity      | Black, non  Mexican Am  Hispanic O  Non Hispan  White non

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

Black, non Hispanic |          0           0           0           0           1

   Mexican American |          0           0           0           0           1

     Hispanic Other |          0           0           0           0           1

 Non Hispanic Other |          0           0           0           0           1

 White non Hispanic |          1           1           1           1           0

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

 

. poisson count i.meth_num i.feth_num i.cross*

 

Iteration 0:   log likelihood = -1628492.6 

Iteration 1:   log likelihood = -514542.18  (backed up)

Iteration 2:   log likelihood = -405521.33  (backed up)

Iteration 3:   log likelihood = -241959.01  (backed up)

Iteration 4:   log likelihood = -147645.83 

Iteration 5:   log likelihood = -59259.102 

Iteration 6:   log likelihood = -12308.206 

Iteration 7:   log likelihood = -10257.604 

Iteration 8:   log likelihood =  -10246.31 

Iteration 9:   log likelihood = -10246.309 

 

Poisson regression                                Number of obs   =         25

                                                  LR chi2(12)     = 3132470.69

                                                  Prob > chi2     =     0.0000

Log likelihood = -10246.309                       Pseudo R2       =     0.9935

 

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

              count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

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

           meth_num |

  Mexican American  |   -.977803   .0181359   -53.92   0.000    -1.013349   -.9422572

    Hispanic Other  |  -1.938943    .020284   -95.59   0.000    -1.978698   -1.899187

Non Hispanic Other  |  -2.506451   .0216409  -115.82   0.000    -2.548866   -2.464035

White non Hispanic  |   .6765227   .0185343    36.50   0.000     .6401961    .7128493

                    |

           feth_num |

  Mexican American  |  -.0199891    .018195    -1.10   0.272    -.0556507    .0156725

    Hispanic Other  |  -.8181779   .0202753   -40.35   0.000    -.8579168    -.778439

Non Hispanic Other  |  -1.107257   .0209967   -52.73   0.000     -1.14841   -1.066104

White non Hispanic  |   1.871097   .0185343   100.95   0.000      1.83477    1.907423

                    |

           1.cross1 |  -3.026666    .017803  -170.01   0.000    -3.061559   -2.991773

           1.cross2 |  -1.014698   .0101682   -99.79   0.000    -1.034627   -.9947684

           1.cross3 |  -.5411649   .0113079   -47.86   0.000     -.563328   -.5190018

           1.cross4 |  -1.444942   .0092927  -155.49   0.000    -1.463155   -1.426728

              _cons |   10.65775   .0048495  2197.69   0.000     10.64825    10.66726

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

 

. poisgof

 

         Deviance goodness-of-fit =  20262.62

         Prob > chi2(12)          =    0.0000

 

         Pearson goodness-of-fit  =  21598.27

         Prob > chi2(12)          =    0.0000

 

. drop score

 

. gen score=meth_num*feth_num

 

. table meth_num feth_num, contents(mean score) cellwidth(10)

 

note: cellwidth too small, variable name truncated;

      to increase cellwidth, specify cellwidth(#)

 

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

husband's           |                   wife's race/ethnicity                  

race/ethnicity      | Black, non  Mexican Am  Hispanic O  Non Hispan  White non

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

Black, non Hispanic |          1           2           3           4           5

   Mexican American |          2           4           6           8          10

     Hispanic Other |          3           6           9          12          15

 Non Hispanic Other |          4           8          12          16          20

 White non Hispanic |          5          10          15          20          25

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

 

. poisson count i.meth_num i.feth_num score

 

Iteration 0:   log likelihood = -448819.89 

Iteration 1:   log likelihood = -199150.22  (backed up)

Iteration 2:   log likelihood = -72023.171 

Iteration 3:   log likelihood = -48477.638 

Iteration 4:   log likelihood = -46237.124 

Iteration 5:   log likelihood = -46234.659 

Iteration 6:   log likelihood = -46234.659 

 

Poisson regression                                Number of obs   =         25

                                                  LR chi2(9)      = 3060493.99

                                                  Prob > chi2     =     0.0000

Log likelihood = -46234.659                       Pseudo R2       =     0.9707

 

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

              count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

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

           meth_num |

  Mexican American  |  -1.712384   .0084087  -203.64   0.000    -1.728865   -1.695903

    Hispanic Other  |   -4.42779     .01369  -323.43   0.000    -4.454622   -4.400958

Non Hispanic Other  |  -7.618328   .0212675  -358.21   0.000    -7.660012   -7.576645

White non Hispanic  |   -6.52374   .0275075  -237.16   0.000    -6.577654   -6.469826

                    |

           feth_num |

  Mexican American  |  -1.609809   .0084125  -191.36   0.000    -1.626297   -1.593321

    Hispanic Other  |  -4.125007    .012931  -319.00   0.000    -4.150352   -4.099663

Non Hispanic Other  |  -6.986868   .0190834  -366.12   0.000    -7.024271   -6.949465

White non Hispanic  |  -6.092624   .0254213  -239.67   0.000    -6.142449   -6.042799

                    |

              score |   .6448603   .0020341   317.02   0.000     .6408736    .6488471

              _cons |   9.695309   .0055428  1749.17   0.000     9.684445    9.706172

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

 

. poisgof

 

         Deviance goodness-of-fit =  92239.32

         Prob > chi2(15)          =    0.0000

 

         Pearson goodness-of-fit  =    123297

         Prob > chi2(15)          =    0.0000

 

. *That was the uniform association model.

 

. nbreg count i.meth_num i.feth_num score

 

Fitting Poisson model:

 

Iteration 0:   log likelihood = -448819.89 

Iteration 1:   log likelihood = -199150.22  (backed up)

Iteration 2:   log likelihood = -72023.171 

Iteration 3:   log likelihood = -48477.638 

Iteration 4:   log likelihood = -46237.124 

Iteration 5:   log likelihood = -46234.659 

Iteration 6:   log likelihood = -46234.659 

 

Fitting constant-only model:

 

Iteration 0:   log likelihood = -279.13989 

Iteration 1:   log likelihood = -248.51927 

Iteration 2:   log likelihood = -248.45842 

Iteration 3:   log likelihood = -248.45835 

Iteration 4:   log likelihood = -248.45835 

 

Fitting full model:

 

Iteration 0:   log likelihood = -243.05506  (not concave)

Iteration 1:   log likelihood = -233.78896  (not concave)

Iteration 2:   log likelihood = -229.91714 

Iteration 3:   log likelihood = -226.12104 

Iteration 4:   log likelihood =  -224.7236 

Iteration 5:   log likelihood = -224.64999 

Iteration 6:   log likelihood = -224.64972 

Iteration 7:   log likelihood = -224.64972 

 

Negative binomial regression                      Number of obs   =         25

                                                  LR chi2(9)      =      47.62

Dispersion     = mean                             Prob > chi2     =     0.0000

Log likelihood = -224.64972                       Pseudo R2       =     0.0958

 

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

              count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

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

           meth_num |

  Mexican American  |  -1.868956   .8171959    -2.29   0.022    -3.470631   -.2672817

    Hispanic Other  |  -4.044556   1.002073    -4.04   0.000    -6.008583   -2.080529

Non Hispanic Other  |  -6.540186   1.122988    -5.82   0.000    -8.741201   -4.339171

White non Hispanic  |  -6.085678   1.233325    -4.93   0.000     -8.50295   -3.668406

                    |

           feth_num |

  Mexican American  |  -.8690387   .8117657    -1.07   0.284     -2.46007    .7219929

    Hispanic Other  |  -3.111358   .9921977    -3.14   0.002    -5.056029   -1.166686

Non Hispanic Other  |  -5.213732   1.116058    -4.67   0.000    -7.401166   -3.026298

White non Hispanic  |  -4.958452   1.210836    -4.10   0.000    -7.331646   -2.585258

                    |

              score |   .5990573   .0937284     6.39   0.000      .415353    .7827615

              _cons |   9.135195   .5880045    15.54   0.000     7.982727    10.28766

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

           /lnalpha |   .0903066   .2472213                     -.3942383    .5748515

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

              alpha |    1.09451   .2705861                      .6741934    1.776867

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

Likelihood-ratio test of alpha=0:  chibar2(01) = 9.2e+04 Prob>=chibar2 = 0.000

 

* The only test we have here is the chibar test for whether alpha, the overdispersion parameter, is significantly different from zero. Here we reject the null hypothesis that the alpha parameter is zero, which means in this case the nbreg model fits better than its poisson model twin.

 

. poisson count i.meth_num i.feth_num i.cross*

 

Iteration 0:   log likelihood = -1628492.6 

Iteration 1:   log likelihood = -514542.18  (backed up)

Iteration 2:   log likelihood = -405521.33  (backed up)

Iteration 3:   log likelihood = -241959.01  (backed up)

Iteration 4:   log likelihood = -147645.83 

Iteration 5:   log likelihood = -59259.102 

Iteration 6:   log likelihood = -12308.206 

Iteration 7:   log likelihood = -10257.604 

Iteration 8:   log likelihood =  -10246.31 

Iteration 9:   log likelihood = -10246.309 

 

Poisson regression                                Number of obs   =         25

                                                  LR chi2(12)     = 3132470.69

                                                  Prob > chi2     =     0.0000

Log likelihood = -10246.309                       Pseudo R2       =     0.9935

 

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

              count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

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

           meth_num |

  Mexican American  |   -.977803   .0181359   -53.92   0.000    -1.013349   -.9422572

    Hispanic Other  |  -1.938943    .020284   -95.59   0.000    -1.978698   -1.899187

Non Hispanic Other  |  -2.506451   .0216409  -115.82   0.000    -2.548866   -2.464035

White non Hispanic  |   .6765227   .0185343    36.50   0.000     .6401961    .7128493

                    |

           feth_num |

  Mexican American  |  -.0199891    .018195    -1.10   0.272    -.0556507    .0156725

    Hispanic Other  |  -.8181779   .0202753   -40.35   0.000    -.8579168    -.778439

Non Hispanic Other  |  -1.107257   .0209967   -52.73   0.000     -1.14841   -1.066104

White non Hispanic  |   1.871097   .0185343   100.95   0.000      1.83477    1.907423

                    |

           1.cross1 |  -3.026666    .017803  -170.01   0.000    -3.061559   -2.991773

           1.cross2 |  -1.014698   .0101682   -99.79   0.000    -1.034627   -.9947684

           1.cross3 |  -.5411649   .0113079   -47.86   0.000     -.563328   -.5190018

           1.cross4 |  -1.444942   .0092927  -155.49   0.000    -1.463155   -1.426728

              _cons |   10.65775   .0048495  2197.69   0.000     10.64825    10.66726

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

 

. nbreg count i.meth_num i.feth_num i.cross*

 

Fitting Poisson model:

 

Iteration 0:   log likelihood = -1628492.6 

Iteration 1:   log likelihood = -514542.18  (backed up)

Iteration 2:   log likelihood = -405521.33  (backed up)

Iteration 3:   log likelihood = -241959.01  (backed up)

Iteration 4:   log likelihood = -147645.83 

Iteration 5:   log likelihood = -59259.102 

Iteration 6:   log likelihood = -12308.206 

Iteration 7:   log likelihood = -10257.604 

Iteration 8:   log likelihood =  -10246.31 

Iteration 9:   log likelihood = -10246.309 

 

Fitting constant-only model:

 

Iteration 0:   log likelihood = -279.13989 

Iteration 1:   log likelihood = -248.51927 

Iteration 2:   log likelihood = -248.45842 

Iteration 3:   log likelihood = -248.45835 

Iteration 4:   log likelihood = -248.45835 

 

Fitting full model:

 

Iteration 0:   log likelihood = -242.73332  (not concave)

Iteration 1:   log likelihood = -233.56685  (not concave)

Iteration 2:   log likelihood = -225.12744 

Iteration 3:   log likelihood = -223.79639 

Iteration 4:   log likelihood = -218.70413 

Iteration 5:   log likelihood = -213.73583 

Iteration 6:   log likelihood = -213.71902 

Iteration 7:   log likelihood = -213.71901 

 

Negative binomial regression                      Number of obs   =         25

                                                  LR chi2(12)     =      69.48

Dispersion     = mean                             Prob > chi2     =     0.0000

Log likelihood = -213.71901                       Pseudo R2       =     0.1398

 

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

              count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

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

           meth_num |

  Mexican American  |  -1.384657   .6048302    -2.29   0.022    -2.570103   -.1992119

    Hispanic Other  |  -1.817025   .5603073    -3.24   0.001    -2.915207   -.7188427

Non Hispanic Other  |  -2.269184   .5464699    -4.15   0.000    -3.340245   -1.198122

White non Hispanic  |   .7621326   .6156133     1.24   0.216    -.4444473    1.968713

                    |

           feth_num |

  Mexican American  |  -.4742313   .6024889    -0.79   0.431    -1.655088    .7066252

    Hispanic Other  |  -1.087938   .5654124    -1.92   0.054    -2.196126    .0202498

Non Hispanic Other  |  -.9830277   .5456848    -1.80   0.072     -2.05255    .0864949

White non Hispanic  |   1.785483   .6156133     2.90   0.004     .5789029    2.992063

                    |

           1.cross1 |  -2.604259   .5915104    -4.40   0.000    -3.763598    -1.44492

           1.cross2 |  -.6298125   .4950926    -1.27   0.203    -1.600176     .340551

           1.cross3 |   -.956327    .459685    -2.08   0.037    -1.857293   -.0553608

           1.cross4 |  -1.021661   .5446708    -1.88   0.061    -2.089197    .0458737

              _cons |   10.65775   .7338531    14.52   0.000     9.219427    12.09608

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

           /lnalpha |  -.6189361   .2620997                     -1.132642   -.1052301

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

              alpha |   .5385171   .1411452                      .3221809    .9001174

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

Likelihood-ratio test of alpha=0:  chibar2(01) = 2.0e+04 Prob>=chibar2 = 0.000

 

. poisson count i.meth_num i.feth_num cross1 cross2 cross3 cross4

 

Iteration 0:   log likelihood = -1628492.6 

Iteration 1:   log likelihood = -514542.18  (backed up)

Iteration 2:   log likelihood = -405521.33  (backed up)

Iteration 3:   log likelihood = -241959.01  (backed up)

Iteration 4:   log likelihood = -147645.83 

Iteration 5:   log likelihood = -59259.102 

Iteration 6:   log likelihood = -12308.206 

Iteration 7:   log likelihood = -10257.604 

Iteration 8:   log likelihood =  -10246.31 

Iteration 9:   log likelihood = -10246.309 

 

Poisson regression                                Number of obs   =         25

                                                  LR chi2(12)     = 3132470.69

                                                  Prob > chi2     =     0.0000

Log likelihood = -10246.309                       Pseudo R2       =     0.9935

 

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              count |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

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

           meth_num |

  Mexican American  |   -.977803   .0181359   -53.92   0.000    -1.013349   -.9422572

    Hispanic Other  |  -1.938943    .020284   -95.59   0.000    -1.978698   -1.899187

Non Hispanic Other  |  -2.506451   .0216409  -115.82   0.000    -2.548866   -2.464035

White non Hispanic  |   .6765227   .0185343    36.50   0.000     .6401961    .7128493

                    |

           feth_num |

  Mexican American  |  -.0199891    .018195    -1.10   0.272    -.0556507    .0156725

    Hispanic Other  |  -.8181779   .0202753   -40.35   0.000    -.8579168    -.778439

Non Hispanic Other  |  -1.107257   .0209967   -52.73   0.000     -1.14841   -1.066104

White non Hispanic  |   1.871097   .0185343   100.95   0.000      1.83477    1.907423

                    |

             cross1 |  -3.026666    .017803  -170.01   0.000    -3.061559   -2.991773

             cross2 |  -1.014698   .0101682   -99.79   0.000    -1.034627   -.9947684

             cross3 |  -.5411649   .0113079   -47.86   0.000     -.563328   -.5190018

             cross4 |  -1.444942   .0092927  -155.49   0.000    -1.463155   -1.426728

              _cons |   10.65775   .0048495  2197.69   0.000     10.64825    10.66726

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. poisgof

 

         Deviance goodness-of-fit =  20262.62

         Prob > chi2(12)          =    0.0000

 

         Pearson goodness-of-fit  =  21598.27

         Prob > chi2(12)          =    0.0000

 

. log close

      name:  <unnamed>

       log:  C:\Users\mexmi\Documents\newer web pages\Soc_382\logs\3rd loglin intermar class.log

  log type:  text

 closed on:   5 Feb 2019, 11:41:48

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