# Cargar las librerías necesarias
library(readxl)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(stats)
# Cargar los datos
data_admision <- read_excel("admision.xlsx", sheet = "data")
# Eliminar las filas con valores NA
data_admision <- na.omit(data_admision)
# Ajustar el modelo de regresión logística
modelo_logistico <- glm(admitido ~ letras + ciencias + prestigio, data = data_admision, family = binomial)
# Resumen del modelo
summary(modelo_logistico)
##
## Call:
## glm(formula = admitido ~ letras + ciencias + prestigio, family = binomial,
## data = data_admision)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.249705 1.155732 -5.408 6.39e-08 ***
## letras 0.002294 0.001092 2.101 0.0356 *
## ciencias 0.007770 0.003275 2.373 0.0177 *
## prestigio 0.560031 0.127137 4.405 1.06e-05 ***
## ---
## 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: 459.44 on 396 degrees of freedom
## AIC: 467.44
##
## Number of Fisher Scoring iterations: 4
# Calcular los coeficientes exponentiados para interpretar las razones de probabilidad (odds ratio)
exp(coef(modelo_logistico))
## (Intercept) letras ciencias prestigio
## 0.001931023 1.002296593 1.007800402 1.750727449