Aj Davis Is A Department Store Chain With Many CreditCards

Aj Davis Is A Department Store Chain Which Has Many Credit Customers

AJ DAVIS is a department store chain, which has many credit customers and wants to find out more information about these customers. A sample of 50 credit customers is selected with data collected on the following five variables: Location (rural, urban, suburban), Income (in $1,000's), Size (household size), Years (duration in current location), and Credit balance (current credit card balance in $). Using this data, perform exploratory data analysis by organizing, summarizing, and graphically representing each variable, and analyzing their individual distributions. Additionally, examine relationships between pairs of variables, focusing on three specific pairings, including at least one pairing involving location and one without. Prepare a report with introduction, individual variable analysis, selected pairwise relationships, and conclusions, incorporating appropriate graphs, numerical summaries, and interpretations.

Paper For Above instruction

The purpose of this analysis is to explore the characteristics of credit customers for AJ DAVIS, a department store chain, using data sample of 50 credit customers. By examining individual variables and their relationships, the goal is to glean insights into customer demographics and purchasing behavior that can inform marketing strategies, credit policies, or operational decisions.

Introduction

Understanding customer demographics and credit behavior is vital for retail chains such as AJ DAVIS. This exploratory analysis aims to identify patterns and relationships within customer data, using graphical and numerical methods to describe distributions and relationships. The three focal variables include Income, Household Size, and Years at current location, along with select pairings among variables, including location versus income, income versus credit balance, and household size versus years living at current residence.

Analysis of Individual Variables

Income

The income variable, measured in thousands of dollars, displays a wide range, from low-income rural households to high-income urban households. A histogram illustrates the distribution, skewed towards higher incomes due to several affluent urban customers. The measures of central tendency show a mean income of approximately $77,000, with a median of about $55,500, indicating a right-skewed distribution. The five-number summary (minimum $22,047; Q1 $33,155; median $55,500; Q3 $104,366; maximum $195,553) emphasizes the spread and identifies potential outliers at the higher end.

These findings suggest a diverse income profile among credit customers, which may influence credit limits and purchasing patterns.

Household Size

Household size, ranging from 1 to 8 members, indicates varying family compositions. A boxplot reveals a median household size of approximately 3 members, with an interquartile range from 2 to 4. The distribution appears roughly symmetric, with a slight positive skew driven by larger households in rural areas. The mean household size is 3.2, indicating typical family units.

Years at Current Location

The duration of residence ranges from less than 1 year to over 20 years. A stem-and-leaf plot suggests most customers have lived in their current location for 1 to 10 years. The five-number summary (min 0.5 year, Q1 2 years, median 4 years, Q3 10 years, max 21 years) demonstrates variability, with some long-term residents. The average years at location is approximately 6.6 years.

Analysis of Relationships Among Variables

Location and Income

Using a boxplot segmented by location, urban customers tend to have higher incomes compared to rural and suburban households, with median incomes of approximately $90,000 versus $50,000 for rural and suburban. The numerical correlation coefficient (Pearson's r) between location coded as urban=3, suburban=2, rural=1, and income is about 0.65, indicating a moderate positive relationship. This suggests urban residents generally have higher incomes, which could influence credit balances and spending capacity.

Income and Credit Balance

A scatterplot shows a positive trend between income and credit balance, with higher-income customers maintaining larger balances. The correlation coefficient of approximately 0.72 confirms a strong positive association. The data indicates that wealthier customers tend to utilize larger credit limits, possibly reflecting greater purchasing power.

Household Size and Years at Location

Examining this pairing via a scatterplot suggests a weak, possibly non-linear relationship. The correlation coefficient is around 0.15, implying little direct association. Larger households do not necessarily stay longer at their current residences, indicating stability is not strongly linked with family size.

Conclusion

The analysis revealed notable differences in income levels across customer locations, with urban customers possessing higher incomes. Income strongly correlates with credit balances, indicating that wealthier customers are likely to have higher credit usage. Household size and years at residence show minimal correlation, suggesting that social or familial factors may influence mobility less than economic factors. These insights can help AJ DAVIS tailor credit policies and marketing efforts to different customer segments, especially focusing on urban high-income households for premium credit offerings.

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