# Case Study Consumer Research Inc.

993 Words May 24th, 2005 4 Pages
This case study included information on a sample of fifty credit card accounts. This information, table one, included household size, annual income, and the amount charged to the account. Scatter plots of the data were produced. Figure one shows household size vs. amount charged. This graph shows that the positive linear relationship of the data is somewhat strong. The r squared is 0.56, analyzing the graph there is a correlation of household size to amount charged, but there is a range per household size. Figure two shows annual income vs. amount charged. The linear relationship of the data is weak, with an r squared of 0.398. Though a positive linear relationship is present. The last scatter plot, Figure 3, shows household …show more content…
Spending habits would show us where most of their money is going. Who is making only large purchases with their cards and who is using their cards for daily expenses.
The average unpaid monthly balance would help determine who is spending more than they can pay back, and who is using their card and then paying it all off at once. This could be used for interest rate analysis. Lower rates for people paying it all off at once to try to coerce them to make larger purchases on their cards and collect interest payments as well as the processing fees from the companies.
Regional cost of living adjustment on the data would help determine the actual value of their annual income and purchases. This would take some of the error out of the data that is collected. For example \$1 in California might only be worth \$0.93 in Indiana, this isn 't very much but when multiplied into the thousands or tens of thousands in grows very large.
Age would also help in determining spending habits. Developing families tend to spend a lot more money on large purchases like appliances and furniture. Lowering their interest rates could coerce them into making more purchases and longer term balances on their cards. Knowing what other outlying debts customers have could be helpful in determining high-risk customers. Along with past credit history this could be helpful in determining customers to reject. There are many factors that affect the amount people charge