Survey: Probit regression
Number of strata = 3 Number of obs = 527
Number of PSUs = 19 Population size = 3,719,863
Design df = 16
F( 5, 12) = .
Prob > F = .
Linearized
schoolenroll3 Coef. Std. Err. t P>t [95% Conf. Interval]
1.male -.0885022 .0240079 -3.69 0.002 -.1393966 -.0376079
HeightZscor .2242943 .0418715 5.36 0.000 .1355307 .313058
male#c.HeightZscor
1 -.2325459 .0393101 -5.92 0.000 -.3158796 -.1492122
c.HeightZscor#c.HeightZscor -.0196331 .007633 -2.57 0.020 -.0358143 -.0034519
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Hello,
i have this probit model.
Dep variable is a dummy variable, school enrollment.
Regressors are:
male is a dummy
HeightZscore is a contionous variable representing child's health.

i did
quietly margins, at ( HeightZscoreP=(-5(1)6)) over (male)
marginsplot
to get the marginal effect of Height score separately for males and females and for the whole sample GRAPHS ATTACHED


I did simulations to the sample, improving child's health while keeping every other regressor as it is to see the effect on the predicted probability of school enrollment.
so the effect on simulations of (80%, 85%, 90% 95% of the median height of children)--representing the effect of a successful nutrition program on the prob of school enrollment.

For males: the average of predicted probability keeps on decreasing
females: decreases and then increases
total: decreases then increases

so the graphs support that it does have a non linear relationship
I want to know the effect of simulations (improving height) on predicted probability are they supporting the non linear relation?