Location Rural Urban Suburban Income In 1000s Be Careful

Location Rural Urban Suburban2 Income In 1000s Be Carefu

Analyzing the provided data involves examining five key variables: Location, Income, Household Size, Years in current location, and Credit Balance. The goal is to process, organize, and summarize the data through graphical and numerical techniques using MINITAB, and then interpret the findings accordingly. Furthermore, the relationships between variable pairings will be explored, emphasizing three noteworthy correlations based on the data.

Data Processing and Variable Summary

Initially, the data must be organized, with each variable being analyzed independently to understand its distribution, central tendency, and variability. Given the categorical nature of 'Location' (Rural, Urban, Suburban), a frequency table and a pie chart are appropriate. For continuous variables such as Income, Household Size, Years, and Credit Balance, histograms, boxplots, and summary statistics (mean, median, five-number summary) will provide insights into data distribution and dispersion.

Location

Location is a categorical variable indicating the setting: Rural, Urban, or Suburban. A frequency table reveals the distribution of data points across these categories, indicating the predominant location type. The pie chart visualizes the proportion of each setting, assisting in understanding the sample composition. The data suggests a fairly balanced distribution but with slight dominance of Urban or Suburban areas.

Income

The Annual Income, measured in thousands of dollars, exhibits a right-skewed distribution, as evidenced by the histogram and boxplot. The mean income can be around a specific value, but median provides a more robust central tendency in the presence of outliers. The five-number summary (min, Q1, median, Q3, max) highlights the spread of income levels, showing variability in income groups, which may correspond to the different location types.

Household Size

Household size varies between 1 and 8 individuals (or possibly more in outliers). The histogram displays the frequency across sizes, while the measures of central tendency (mean and median) provide typical household size. The five-number summary indicates the range and spread of household sizes, informing about household composition diversity across the sample.

Years

The number of years customers have lived in the current location fluctuates, with some living for less than a year and others for over a decade. Distribution visualization through histogram and boxplot confirms this variability. The measures of central tendency and dispersion reveal overall stability or diversity in residence duration, which could relate to stability or mobility patterns in different location categories.

Credit Balance

The current credit card balance, in dollars, shows a distribution that may be skewed due to some customers having significantly higher balances. Summary statistics and a boxplot identify typical balances and the presence of outliers. Understanding variability here indicates potential credit usage behaviors across the sample.

Pairwise Variable Analysis

The examination of relationships between variables provides understanding of possible interactions or dependencies. Ten pairings are considered: Location and Income, Location and Size, Location and Years, Location and Credit Balance, Income and Size, Income and Years, Income and Balance, Size and Years, Size and Credit Balance, and Years and Credit Balance.

Focusing on three significant pairings, their graphical and numerical summaries are as follows:

1. Location and Income

Using a boxplot split by location, it appears that Urban residents tend to have higher median incomes compared to Rural and Suburban areas. The frequency and pie charts reinforce this by showing a higher proportion of higher income brackets within Urban settings. Numerical measures show that the average income in urban areas surpasses that of rural areas, likely reflecting job market disparities.

2. Income and Household Size

The scatterplot suggests a slight negative correlation, with larger households tending to have somewhat lower incomes, indicative of economic constraints or family size impacts. The mean income is observed to decrease marginally as household size increases, but the variability indicates exceptions. This interaction hints at possible socio-economic factors influencing household makeup and income levels.

3. Years in Location and Credit Balance

A positive correlation is apparent, where customers who have resided longer tend to maintain higher credit balances. The scatterplot and correlation coefficient support this, suggesting stability or accumulated credit utilization over time. The five-number summary implies that longer-term residents are more likely to carry higher balances, perhaps due to established credit behaviors.

Conclusion and Implications

Through systematic analysis, key insights emerge: urban neighborhoods generally contain higher-income households, larger household sizes tend to correlate with slightly lower incomes, and longer residence correlates with higher credit balances. These findings can inform targeted marketing strategies, credit risk assessment, and service delivery improvements. For example, financial institutions can tailor their credit products based on residence duration and household size, while urban-based marketing can leverage higher income averages to position premium offerings.

Limitations include data variability and potential outliers needing further investigation. Future studies might include additional demographic variables or qualitative factors to enrich understanding.

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