library(rio)
admit=import("dataAdmit.xlsx")
#gre+gpa+pres
str(admit)
## 'data.frame': 400 obs. of 4 variables:
## $ admitido : chr "no" "si" "si" "si" ...
## $ gre : num 380 660 800 640 520 760 560 400 540 700 ...
## $ gpa : num 3.61 3.67 4 3.19 2.93 3 2.98 3.08 3.39 3.92 ...
## $ prestigio: chr "Bajo" "Bajo" "MuyAlto" "MuyBajo" ...
names(admit)
## [1] "admitido" "gre" "gpa" "prestigio"
admit$admitido=as.factor(admit$admitido)
admit$prestigio=as.factor(admit$prestigio)
rlog1=glm(admitido ~ gre+gpa+prestigio,data = admit, family = binomial(link = "logit"))
summary(rlog1)
##
## Call:
## glm(formula = admitido ~ gre + gpa + prestigio, family = binomial(link = "logit"),
## data = admit)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6268 -0.8662 -0.6388 1.1490 2.0790
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.665422 1.109370 -4.205 2.61e-05 ***
## gre 0.002264 0.001094 2.070 0.0385 *
## gpa 0.804038 0.331819 2.423 0.0154 *
## prestigioBajo -0.664761 0.283319 -2.346 0.0190 *
## prestigioMuyAlto 0.675443 0.316490 2.134 0.0328 *
## prestigioMuyBajo -0.876021 0.366735 -2.389 0.0169 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 499.98 on 399 degrees of freedom
## Residual deviance: 458.52 on 394 degrees of freedom
## AIC: 470.52
##
## Number of Fisher Scoring iterations: 4