Introduction
AJ Davis, a large department store chain, has enlisted my company to help them find out more about their customers who shop using credit. A sample of 50 credit customers were selected based on five variables which included location, income, size, years, and credit balance.
Location (Rural, Urban, Suburban)
Income (in $1,000s)
Size (Household Size)
Years (Number of Years That the Customer Has Lived in the Current Location)
Credit Balance (The Customers Current Credit Card Balance on the Store’s Credit Card)
We will take a look at three different variables at this time.
Location
The location variable has three subcategories which include rural, urban, and suburban. This variable is looking at where customers live.
A pie chart is a circular graph which divides information into sections based on numerical proportions. Frequency distribution tables allow us to look at variables and their frequencies (or how many times the variables occur).
Frequency Distribution
Location Frequency (# of Customers)
Urban 21
Suburban 15
Rural 14

Interpretation: Based on the information shown in both the pie chart and the frequency distribution chart, we can see that more of the customers (21/50 = 42%) are from urban areas. Suburban areas are next with (15/50) 30% of the customers and rural areas have the least amount of customers with (14/50) 28%.

Credit Balance
The credit balance is the amount of funds that are currently charged to the credit card. Credit balance is a quantitative variable which means we actually can get an exact numerical value.
A histogram is a graph of frequency distribution using rectangles.

Descriptive Statistics: CREDIT BALANCE($)

Total
Variable Count N N* CumN Percent CumPct Mean SE Mean StDev
CREDIT BALANCE($) 50 50 0 50 100 100 3966 132 936

Variable Minimum Q1 Median Q3 Maximum Skewness
CREDIT BALANCE($) 1864 3109 4048 4778 5678 -0.13

Relative frequency shows the relative frequency of a class divided by the total number of frequencies of the class. The relative frequency distribution shows this data in chart form for easy viewing.
Relative Frequency Distribution
Credit Balance ($) Frequency Relative Frequency
2000 1 1/50= 0.02 = 2%
2500 5 5/50= 0.1 = 10%
3000 8 8/50= 0.16 = 16%
3500 6 6/50= 0.12 = 12%
4000 12 12/50= 0.24 = 24%
4500 5 5/50= 0.1 = 10%
5000 8 8/50= 0.16 = 16%
5500 5 5/50= 0.1 = 10% Total: 50 Total: 100%

Interpretation: The mean credit balance of the customers is $3966. The standard deviation is 936.4 If you look at the histogram you can look at the peak of the bell-shaped distribution and note that it has peaked around $4000. The relative distribution chart tells us that 24% of cardholders carry an approximate $4000 balance. This means that a greater number of customers have a credit balance in this range.

Household Size
This variable looks at the number of people who live in the household.

Descriptive Statistics: SIZE

Total
Variable Count CumN Percent CumPct Mean SE Mean StDev Variance
SIZE 50 50 100 100 3.420 0.246 1.739 3.024

N for
Variable Minimum Median Maximum Range Mode Mode Skewness
SIZE 1.000 3.000 7.000 6.000 2 15 0.53

Frequency Distribution of Household Size

Size of Household Frequency

1 5
2 15
3 8
4 9
5 5
6 5
7 3 Total: 50

The mean household size of the customers is 3.420. The median is 3.000. The mode for household size for the customers is 2 and the N for the mode is 15. As evidenced by the frequency distribution and the bar chart, more customers live in households containing two people.

Pairings of Variables
Location vs. Household Size The bar chart is looking at the pairing of the size of the household and the location of the household.

Tabulated statistics: LOCATION, SIZE

Rows: LOCATION Columns: SIZE

1 2 3 4 5 6 7 All

Rural 5 8 0 1 0 0 0 14
Suburban 0 2 2 5 2 3 1 15
Urban 0 5 6 3 3 2 2 21
All 5 15 8 9 5 5 3 50

Previous data analysis has already shown that a majority of customers live in urban areas, then suburban areas, and lastly rural areas. The pairing of location and size tells us the varying sizes of households in each location. From the statistics that were tabulated we can see that in rural areas 57% of the households contained 2 people and 2.8% contained 4 people. In the suburban areas, 33% of the households contained 5 people. In the urban areas, 29% of the households contained 3 people.

Income vs. Credit Balance When looking at the scatter plot, a positive linear relationship is shown between Income vs. Credit Balance. As the graph shows, for the most part, a higher income is associated with a higher credit balance.

Credit Balance vs. Years When looking at the comparison of Credit Balance vs. Years (years the customers has lived in the current location), there is no clear relationship between the two variables.

Conclusion
In looking at the variables that were compared, there was a strong correlation between credit balance and income. The other variables, however, did not show significant relationships.