Regresi Logistik

Y <- c(1,0,1,0,0,0,1,0,0,0,1,1,0,1,1,0,1,1,1,0)
x1 <- c(1,1,0,1,0,0,0,1,1,0,0,1,0,1,0,1,1,1,0,0)
x2 <- c(1,0,1,1,1,0,1,0,0,0,1,1,0,1,0,0,0,0,1,1)
data <- data.frame(Y,x1,x2)

 reglog<-glm(Y~x1+x2,family=binomial,data=data)
summary(reglog)

## 
## Call:
## glm(formula = Y ~ x1 + x2, family = binomial, data = data)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  -1.0986     0.9522  -1.154   0.2486  
## x1            0.4055     1.0206   0.397   0.6912  
## x2            1.7918     1.0206   1.756   0.0792 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 27.726  on 19  degrees of freedom
## Residual deviance: 24.274  on 17  degrees of freedom
## AIC: 30.274
## 
## Number of Fisher Scoring iterations: 4