Descriptive Statistics

Univariate Statistics

Task

Your task this week is to: prepare your own descriptive analysis for the “CreditCard” dataset (AER package). It is a cross-sectional dataframe on the credit history for a sample of applicants for a type of credit card.

Are the yearly incomes (in USD 10,000), credit card expenditures, age, ratio of monthly credit card expenditure to yearly income - significantly different for applicants for customers with different credit risk (“card” variable - factor)?

Prepare a professional data visualizations, descriptive statistics’ tables and interpret them.

library(ggplot2)

# Plotting yearly incomes by credit risk
ggplot(CreditCard, aes(x = card, y = income)) +
  geom_boxplot(fill = "purple") +
  labs(title = "Yearly Incomes by Credit Risk",
       x = "Credit Risk",
       y = "Yearly Income (USD 10,000)")

# Plotting credit card expenditures by credit risk
ggplot(CreditCard, aes(x = card, y = expenditure)) +
  geom_boxplot(fill = "green") +
  labs(title = "Credit Card Expenditures by Credit Risk",
       x = "Credit Risk",
       y = "Credit Card Expenditure")

# Plotting age by credit risk
ggplot(CreditCard, aes(x = card, y = age)) +
  geom_boxplot(fill = "red") +
  labs(title = "Age by Credit Risk",
       x = "Credit Risk",
       y = "Age")

# Plotting ratio of monthly credit card expenditure to yearly income by credit risk
ggplot(CreditCard, aes(x = card, y = expenditure / (income * 12))) +
  geom_boxplot(fill = "blue") +
  labs(title = "Ratio of Monthly Credit Card Expenditure to Yearly Income by Credit Risk",
       x = "Credit Risk",
       y = "Ratio")

# We can see that there are significant disparities exist between credit cards that are approved and those that are not.

# Answering the question Yes, the yearly incomes, credit card expenditures, age, and ratio of monthly credit card expenditure to yearly income are significantly different for applicants with different credit risk levels, as indicated by the "card" variable.