Actual StataMP numerical output. Log header and machine paths omitted.

. * BEGIN lpm
. regress deny pir, vce(robust)

Linear regression                               Number of obs     =      2,380
                                                F(1, 2378)        =      37.56
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0397
                                                Root MSE          =     .31828

------------------------------------------------------------------------------
             |               Robust
        deny | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         pir |   .6035349   .0984826     6.13   0.000     .4104144    .7966555
       _cons |  -.0799096   .0319666    -2.50   0.012    -.1425949   -.0172243
------------------------------------------------------------------------------

. * END lpm
. record lpm pir

. scalar a_lpm=_b[_cons]

. scalar b_lpm=_b[pir]

. * BEGIN probit
. probit deny pir, vce(robust)

Iteration 0:  Log pseudolikelihood =  -872.0853  
Iteration 1:  Log pseudolikelihood = -832.02975  
Iteration 2:  Log pseudolikelihood = -831.79239  
Iteration 3:  Log pseudolikelihood = -831.79234  

Probit regression                                       Number of obs =  2,380
                                                        Wald chi2(1)  =  40.68
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -831.79234                       Pseudo R2     = 0.0462

------------------------------------------------------------------------------
             |               Robust
        deny | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         pir |   2.967907   .4653217     6.38   0.000     2.055893    3.879921
       _cons |  -2.194159   .1649761   -13.30   0.000    -2.517506   -1.870812
------------------------------------------------------------------------------
Note: 0 failures and 1 success completely determined.

. * END probit
. record probit pir

. scalar a_probit=_b[_cons]

. scalar b_probit=_b[pir]

. * BEGIN probabilities
. margins, at(pir=(.3 .4 .5))

Adjusted predictions                                     Number of obs = 2,380
Model VCE: Robust

Expression: Pr(deny), predict()
1._at: pir = .3
2._at: pir = .4
3._at: pir = .5

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0961532   .0069017    13.93   0.000      .082626    .1096803
          2  |   .1569683   .0101166    15.52   0.000     .1371402    .1767965
          3  |   .2387884    .024485     9.75   0.000     .1907987    .2867781
------------------------------------------------------------------------------

. * END probabilities
. * BEGIN ame
. margins, dydx(pir)

Average marginal effects                                 Number of obs = 2,380
Model VCE: Robust

Expression: Pr(deny), predict()
dy/dx wrt:  pir

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         pir |   .5664828   .0866687     6.54   0.000     .3966153    .7363504
------------------------------------------------------------------------------

. * END ame
. * BEGIN logit
. logit deny pir, vce(robust)

Iteration 0:  Log pseudolikelihood =  -872.0853  
Iteration 1:  Log pseudolikelihood = -830.96071  
Iteration 2:  Log pseudolikelihood = -830.09497  
Iteration 3:  Log pseudolikelihood = -830.09403  
Iteration 4:  Log pseudolikelihood = -830.09403  

Logistic regression                                     Number of obs =  2,380
                                                        Wald chi2(1)  =  34.63
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -830.09403                       Pseudo R2     = 0.0482

------------------------------------------------------------------------------
             |               Robust
        deny | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         pir |   5.884498   .9999333     5.88   0.000     3.924664    7.844331
       _cons |  -4.028432   .3589036   -11.22   0.000     -4.73187   -3.324994
------------------------------------------------------------------------------

. * END logit
. record logit pir

. scalar a_logit=_b[_cons]

. scalar b_logit=_b[pir]

. preserve

. clear

. set obs 151
Number of observations (_N) was 0, now 151.

. generate double pir=(_n-1)/100

. generate double lpm=a_lpm+b_lpm*pir

. generate double probit=normal(a_probit+b_probit*pir)

. generate double logit=invlogit(a_logit+b_logit*pir)

. export delimited using "output/meeting_15_curves.csv", replace
(file output/meeting_15_curves.csv not found)
file output/meeting_15_curves.csv saved

. restore

. 
. postclose results

. use "output/meeting_15_estimates.dta", clear

. export delimited using "output/meeting_15_estimates.csv", replace
(file output/meeting_15_estimates.csv not found)
file output/meeting_15_estimates.csv saved

. display "ECO603_MEETING_15_COMPLETE"
ECO603_MEETING_15_COMPLETE