AIDS in the Circle City

Financial Correlations

Correlations

Maps

References

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                For a sixth and final set of correlations, I decided to examine the relationships between finances and cumulative AIDS cases in the given zip codes.  I discovered the median household income of households in Marion County correlated with the Cumulative AIDS cases.  As the median household income increased, the number of cumulative AIDS cases in a zip code decreased, giving a negative correlation with a correlation coefficient of 0.590.  The scatter plot looks as follows:

            There is a very clear negative correlation.  This is a beautiful scatter plot.  Clearly as the income increases, the AIDS cases decrease.  The zip codes toward the bottom of the page are the ones that are farther away from the city center.  While the one with the arrow is 46202, the zip code with the highest number of AIDS cases and located in the heart of the city.

            Along with median household income, I also looked at the percentage of households receiving Public Assistance Income.  The percent of households receiving public assistance correlated beautifully with the number of cumulative AIDS cases.  They correlated with a strong positive coefficient of 0.597 just as I thought they would.  As is shown by the scatter plot above, the lower the income, the higher the AIDS cases.  Households receiving Public Assistance must qualify by being under a certain income level, making them the poorer households in Marion County.

            On the other hand, the households not receiving Public Assistance Income had, as expected, a negative correlation.  The coefficient was exactly that of the households receiving public assistance, but negative.  I found it interesting that the correlation coefficients were exactly the same.  I do not quite know that this is significant, but it is interesting nonetheless.

            As is clear, as the percentage of households not receiving public assistance increases, the number of cumulative AIDS cases decreases, giving a negative correlation.  The scatter plots above are almost identically opposite of each other, which makes sense.

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