Orange County New York

The Race Question...
 

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           Before I correlated the race demographics with the AIDS rates, I didn’t know what to expect.  For instance, before I knew the difference between AIDS and HIV, I believed that women of color were the most in risk of AIDS.  After receiving my data from the Orange County Health Department, I realized that in Orange County, New York mostly white males have actually died of the disease.  Even though more whites died of AIDS than blacks, the rates for blacks were considerably high in Orange County.  The national cumulative rate for blacks is 1117, and the Orange County rate is 1612.  Because of my prior knowledge from the last paper, I was pretty sure that there would be a relationship between race and the AIDS rates, especially for blacks. 

         The correlation coefficient for white total population was -0.3935, which is between p=.05 and p=.025.  Therefore, using the p-values I am 96% confident that there is a indirect relationship between white percentage and AIDS rates.  The negative correlation of total population of whites signifies that if the white population increases (decreases) than the AIDS rate decreases (increases): they have an indirect relationship.  The zip-codes with the highest white population have the lowest AIDS rate.  Is this the reason why people commonly assume that AIDS is a disease for non-whites?  Interestingly, Otisville has the second lowest population of whites in Orange County.  Although whites are still the majority, does this still imply that a large amount of the prison population is non-white?  I was unsure what to expect regarding the correlation o blacks and AIDS rates.

 

 

       I live in a suburban/rural area; therefore the black population is very small.  I thought the small numbers of the population would not impact the relationship with AIDS rates.  Surprisingly, the total black population and AIDS rates have a convincing correlation of +0.5381 leaving the p-value as low as .01.  Since the correlation is positive, it demonstrates a linear relationship.  For example, where the black population increases (decreases), the AIDS rates should also increase (decrease).  Based on these strong results, I am 99% confident that there is a relation between blacks and AIDS rates in Orange County.  I also looked at my results of Hispanic percentage because the correlation of blacks is so convincing.  Since, this had the second largest race rate; I believed a correlation must exist between these numbers.  In fact, a correlation of +0.40 did exist between the Hispanic population and AIDS rate with a p-value of p=. 025; therefore there is a 97% confidence level of Hispanics and AIDS.  While I was constructing my graphs, I realized that there was a consistent point on each of my race graph that was visibly separated from the rest of my data.  I realized that even though the statistical calculation can provide a significant correlation, the positive correlation may in fact be the result of a single point, which was Otisville (10936).  I calculated the correlations with out Otisville, and I found no confident correlations.  For example, blacks have a correlation of 0.2548; Hispanics have a correlation of 0.1185; and whites don’t even correlate.  Although the significance and the p-value change, the same pattern still is illustrated, which is that non-whites correlate better with AIDS cases than whites.

Table 1     Spread Sheet