Install packages, get the dataset “teengamb”, and see the summary
#install.packages('faraway')
library(faraway)
data(teengamb)
summary(teengamb)
##       sex             status          income           verbal     
##  Min.   :0.0000   Min.   :18.00   Min.   : 0.600   Min.   : 1.00  
##  1st Qu.:0.0000   1st Qu.:28.00   1st Qu.: 2.000   1st Qu.: 6.00  
##  Median :0.0000   Median :43.00   Median : 3.250   Median : 7.00  
##  Mean   :0.4043   Mean   :45.23   Mean   : 4.642   Mean   : 6.66  
##  3rd Qu.:1.0000   3rd Qu.:61.50   3rd Qu.: 6.210   3rd Qu.: 8.00  
##  Max.   :1.0000   Max.   :75.00   Max.   :15.000   Max.   :10.00  
##      gamble     
##  Min.   :  0.0  
##  1st Qu.:  1.1  
##  Median :  6.0  
##  Mean   : 19.3  
##  3rd Qu.: 19.4  
##  Max.   :156.0
Designate categorical variables (sex: 0-male, 1-female) as factors.
teengamb$sex <- factor(teengamb$sex)
teengamb[1:5,]
##   sex status income verbal gamble
## 1   1     51    2.0      8    0.0
## 2   1     28    2.5      8    0.0
## 3   1     37    2.0      6    0.0
## 4   1     28    7.0      4    7.3
## 5   1     65    2.0      8   19.6
levels(teengamb$sex) <- c('male', 'female')
summary(teengamb$sex)
##   male female 
##     28     19
hist(teengamb$status, breaks=20)

hist(teengamb$income, breaks=20)

hist(teengamb$verbal, breaks=20)

Plotting gamble amount against stuatus:
plot(gamble ~ status, teengamb)


So it seems that there is a negative correlation between these two variables.

Plotting gamble amount against income:
plot(gamble ~ income, teengamb)


So it seems that there is a positive correlation between these two variables.

Plotting gamble amount against verbal score:
plot(gamble ~ verbal, teengamb)


So it seems that there is a negative correlation between these two variables.

Plotting gamble amount against sex:
plot(gamble ~ sex, teengamb)


So it seems that in general males are more likely to put more money on gambling.