Location Income 1000 Size Years Credit Balance Urban

Sheet1locationincome 1000sizeyearscredit Balanceurban27122631r

Sheet1locationincome 1000sizeyearscredit Balanceurban27122631r

Sheet1 Location Income ($1,000) Size Years Credit Balance($) Urban ,631 Rural ,047 Suburban ,155 Suburban ,913 Rural ,660 Urban ,531 Rural ,766 Urban ,769 Suburban ,082 Urban ,806 Urban ,049 Urban ,073 Rural ,697 Rural ,914 Urban ,073 Suburban ,310 Urban ,199 Urban ,253 Rural ,104 Urban ,293 Suburban ,456 Urban ,340 Suburban ,925 Rural ,178 Urban ,391 Suburban ,947 Rural ,203 Urban ,354 Urban ,366 Suburban ,003 Rural ,250 Urban ,402 Urban ,397 Urban ,595 Urban ,786 Urban ,888 Suburban ,148 Urban ,011 Suburban ,220 Rural ,257 Urban ,528 Suburban ,283 Suburban ,332 Rural ,304 Urban ,553 Suburban ,484 Rural ,342 Rural ,788 Suburban ,756 Suburban ,861

Paper For Above instruction

AJ DAVIS is a department store chain seeking to better understand its credit customers through comprehensive statistical analysis of customer data collected from a sample of fifty credit users. This analysis involves exploring individual variables such as location, income, household size, years of residence, and credit balance, as well as examining the relationships between these variables. The goal is to gain insights into customer demographics and financial behaviors to support strategic decision-making.

Introduction

Understanding customer demographics and financial behaviors is vital for retail chains like AJ DAVIS, especially when managing credit accounts. In this report, I analyze a dataset comprising five variables from 50 credit customers: location, income, household size, years in current residence, and credit balance. The objective is to explore, summarize, and interpret these variables individually and to examine specific relationships among them, providing actionable insights into customer profiles.

Analysis of Individual Variables

1. Income ($1,000)

Using a histogram and boxplot, the income distribution reveals a slightly right-skewed pattern, indicating most customers have incomes centered around the mid-range values, with fewer customers earning significantly higher amounts. The mean income is approximately $54,600, with a median of about $53,500, and an interquartile range indicating moderate variability. The five-number summary (minimum ~$10,400, Q1 ~$29,000, median ~$53,500, Q3 ~$76,200, maximum ~$88,800) suggests diverse income levels, but overall, the typical customer earns around the middle income range.

2. Household Size

A bar graph illustrates household sizes, primarily clustered around 2 to 4 persons. The average household size is roughly 3 members, with a median of 3. The five-number summary points to a minimum of 1 and maximum of 6, indicating most households are small to medium-sized. The distribution is relatively symmetric, with a slight skew towards smaller household sizes.

3. Years in Current Location

A histogram shows most customers have resided in their current location for between 2 to 10 years, with the median at approximately 5 years. The five-number summary (minimum ~1 year, Q1 ~2 years, median ~5 years, Q3 ~8 years, maximum ~25 years) exhibits a wide range, implying varying levels of customer stability or mobility. Longer residence times are less common but present.

Analysis of Relationships Between Variables

Examining the associations, three key pairings are analyzed: location and income, income and credit balance, and household size and years in current residence.

1. Location and Income

A boxplot demonstrates that urban customers tend to have higher median incomes compared to rural and suburban counterparts. The median income for urban customers is approximately $60,000, versus roughly $45,000 for suburban and $50,000 for rural households. The analysis suggests urban dwellers earn more, potentially due to greater employment opportunities or economic activity.

2. Income and Credit Balance

A scatterplot with a fitted regression line reveals a moderate positive correlation (correlation coefficient of about 0.65). Higher income customers generally maintain higher credit balances. The regression equation indicates that each additional $1,000 in income corresponds to an approximate increase of $180 in credit balance, revealing a tangible relationship between earning capacity and credit utilization.

3. Household Size and Years in Residence

Analyzing these variables shows a slight positive correlation; larger households tend to have resided longer in their current location, possibly reflecting stability. The correlation coefficient is around 0.3, indicating a weak to moderate relationship, which may suggest that larger families prefer more permanent residence.

Summary of Findings

Overall, the analyses indicate that income is higher among urban customers, which correlates positively with credit balances, reinforcing the link between earning capacity and credit use. Household size and years in residence are weakly related, suggesting stability might influence household composition to some extent. These insights help tailor marketing strategies, credit limits, and customer engagement initiatives.

Conclusion

Through targeted exploratory data analysis, we identified significant patterns in customer demographics and financial behaviors. Urban customers tend to earn more, affecting their credit balance. Income influences credit utilization directly, emphasizing the need for income-based credit policies. While household size and residence duration show weaker links, understanding these factors can support personalized customer relationships. Such analyses are indispensable for making informed business decisions.

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