Perform the following steps to fit a generalized linear regression model with the Binomial model:

1. First, we examine the first six elements of vs within mtcars:

        > head(mtcars$vs)
        Output:
    
         [1] 0 0 1 1 0 1  

2. We apply the glm function with binomial as the family object:

        > lm1 = glm(vs ~ hp+mpg+gear,data=mtcars, family=binomial)
        > summary(lm1)
       Output:
       Call:
       glm(formula = vs ~ hp + mpg + gear, family = binomial, data =
       mtcars)

       Deviance Residuals: 
            Min     1Q      Median   3Q     Max 
        -1.68166 -0.23743 -0.00945 0.30884 1.55688 

        Coefficients:
                    Estimate Std. Error z value Pr(>|z|) 
        (Intercept) 11.95183 8.00322 1.493 0.1353 
        hp -0.07322 0.03440 -2.129 0.0333 *
        mpg 0.16051 0.27538 0.583 0.5600 
        gear -1.66526 1.76407 -0.944 0.3452 
        ---
        Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

        (Dispersion parameter for binomial family taken to be 1)

            Null deviance: 43.860 on 31 degrees of freedom
        Residual deviance: 15.651 on 28 degrees of freedom
        AIC: 23.651

        Number of Fisher Scoring iterations: 7