I have the following regression:
Call:
glm(formula = IN_FIN_REEMB_FIES ~ CO_CATEGORIA_ADMINISTRATIVA +
CO_COR_RACA_ALUNO + IN_RESERVA_VAGAS + IN_RESERVA_ENSINO_PUBLICO +
CO_TURNO_ALUNO + IN_RESERVA_RENDA_FAMILIAR + IN_FIN_NAOREEMB_PROUNI_PARCIAL +
TP_PROCEDE_EDUC_PUBLICA + IN_SEXO_ALUNO + NU_IDADE_ALUNO,
family = binomial, data = nor1)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.3535 -0.7779 -0.6651 -0.4668 2.8123
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.3802707 0.0112696 -33.743 < 2e-16 ***
CO_CATEGORIA_ADMINISTRATIVA5 -0.5030216 0.0063270 -79.504 < 2e-16 ***
CO_COR_RACA_ALUNO1 -0.0452001 0.0081445 -5.550 2.86e-08 ***
CO_COR_RACA_ALUNO2 0.2784830 0.0144464 19.277 < 2e-16 ***
CO_COR_RACA_ALUNO3 0.1527192 0.0067474 22.634 < 2e-16 ***
CO_COR_RACA_ALUNO4 0.2388296 0.0210065 11.369 < 2e-16 ***
CO_COR_RACA_ALUNO5 0.0357580 0.0516736 0.692 0.48894
IN_RESERVA_VAGAS 0.7116092 0.0474497 14.997 < 2e-16 ***
IN_RESERVA_ENSINO_PUBLICO -3.1344320 0.1919678 -16.328 < 2e-16 ***
CO_TURNO_ALUNO -0.0157669 0.0055981 -2.816 0.00486 **
IN_RESERVA_RENDA_FAMILIAR -9.8526964 14.0635939 -0.701 0.48356
IN_FIN_NAOREEMB_PROUNI_PARCIAL -0.3676032 0.0206161 -17.831 < 2e-16 ***
TP_PROCEDE_EDUC_PUBLICA1 0.1709269 0.0054290 31.484 < 2e-16 ***
IN_SEXO_ALUNO 0.1497493 0.0055368 27.046 < 2e-16 ***
NU_IDADE_ALUNO -0.0328465 0.0003694 -88.911 < 2e-16 ***
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 876350 on 808601 degrees of freedom
Residual deviance: 857364 on 808587 degrees of freedom
AIC: 857394
Number of Fisher Scoring iterations: 9
I need to create type scenarios: CO_COR_RACA_ALUNO = 1, TP_PROCEDE_EDUC_PUBLICA1 = 1, NU_ALL_ITY