Stat Computing > Stata > Output

This page shows an example on logistic regression analysis with popup windows explaining the output.  The analysis uses a data file about crime. In this example we first create a dichotomuos response variable called hicrime by the command: . generate hicrime = (crimerat >= 110). This model will predict hicrime from maleteen, south, educ and police59 using the following Stata commands (both logistic and logit are use to obtain output with odds ratios and regular coefficients).

. logistic hicrime maleteen south educ police59
logit

lfit
lfit, group(10) 

Output

. logistic hicrime maleteen south educ police59

Logit estimates                                   Number of obs   =         47
                                                  LR chi2(4)      =      13.93
                                                  Prob > chi2     =     0.0075
Log likelihood = -18.606959                       Pseudo R2       =     0.2724

------------------------------------------------------------------------------
hicrime  | Odds Ratio  Std. Err.      z    P>|z|      [95% Conf. Interval]
---------+--------------------------------------------------------------------
maleteen |   1.086959   .0478646      1.894   0.058       .9970804    1.184939
   south |   .3272305   .4449077     -0.822   0.411       .0227796     4.70068
    educ |   1.023187   .5723757      0.041   0.967       .3418133    3.062818
police59 |   1.059909   .0222633      2.770   0.006        1.01716    1.104455
------------------------------------------------------------------------------

logit

Logit estimates                                   Number of obs   =         47
                                                  LR chi2(4)      =      13.93
                                                  Prob > chi2     =     0.0075
Log likelihood = -18.606959                       Pseudo R2       =     0.2724

------------------------------------------------------------------------------
hicrime  |      Coef.  Std. Err.        z    P>|z|      [95% Conf. Interval]
---------+--------------------------------------------------------------------
maleteen |   .0833837   .0440353      1.894   0.058      -.0029239    .1696914
   south |  -1.117091   1.359616     -0.822   0.411      -3.781888    1.547707
    educ |   .0229224   .5594047      0.041   0.967      -1.073491    1.119335
police59 |   .0581834   .0210049      2.770   0.006       .0170147    .0993522
   _cons |  -17.70177   9.495993     -1.864   0.062      -36.31357    .9100364
------------------------------------------------------------------------------

lfit

Logistic model for hicrime, goodness-of-fit test

       number of observations =        47
 number of covariate patterns =        47
             Pearson chi2(42) =        38.72
                  Prob > chi2 =         0.6158


lfit, group(10)

Logistic model for hicrime, goodness-of-fit test
(Table collapsed on quantiles of estimated probabilities)

       number of observations =        47
             number of groups =        10
      Hosmer-Lemeshow chi2(8) =        13.45
                  Prob > chi2 =         0.0972

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Web Notes

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  http://www.ats.ucla.edu/stat/stata/

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