knitr::opts_chunk$set(echo = TRUE)
require(Sleuth3)
require(tigerstats)

Getting the data:

lines <- 
"NEWCAR INCOME  CAR.AGE
0   32  3
0   45  2
1   60  2
0   53  1
0   25  4
1   68  1
1   82  2
1   38  5
0   67  2
1   92  2
1   72  3
0   21  5
0   26  3
1   40  4
0   33  3
0   45  1
1   61  2
0   16  3
1   18  4
0   22  6
0   27  3
1   35  3
1   40  3
0   10  4
0   24  3
1   15  4
0   23  3
0   19  5
1   22  2
0   61  2
0   21  3
1   32  5
0   17  1"

con <- textConnection(lines)
newcars <- read.csv(con, sep="")
fit <- glm(NEWCAR ~ INCOME + CAR.AGE, family=binomial(link="logit"), data=newcars)
summary(fit)
## 
## Call:
## glm(formula = NEWCAR ~ INCOME + CAR.AGE, family = binomial(link = "logit"), 
##     data = newcars)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.6189  -0.8949  -0.5880   0.9653   2.0846  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -4.73931    2.10195  -2.255   0.0242 *
## INCOME       0.06773    0.02806   2.414   0.0158 *
## CAR.AGE      0.59863    0.39007   1.535   0.1249  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 44.987  on 32  degrees of freedom
## Residual deviance: 36.690  on 30  degrees of freedom
## AIC: 42.69
## 
## Number of Fisher Scoring iterations: 4

Coefficients:

exp(fit$coefficients)
## (Intercept)      INCOME     CAR.AGE 
## 0.008744682 1.070079093 1.819627221