Core 116

Conclusions About Correlations

All of my attempts at correlations, however, did not result in the same level of success. Surprisingly, a few of the factors that I tried did not correlate with the AIDS rate. The percentage of households that were owned by male-male or female-female partners, for example, did not correlate with the AIDS rate. I had expected that the presence of a gay community in a town or city might indicate a higher rate of AIDS cases. However, this appears to be untrue in Fairfield County. Instead, a high rate of AIDS cases seems to be better indicated by the presence of African American or Hispanic communities, as I showed above.

I also tried to correlate various commute times to the AIDS rate. I hypothesized that those persons who commuted longer to work (possibly into New York City) would have higher paying jobs and therefore be less likely to acquire AIDS. Instead I found that shorter commute times did not correlate with a higher presence of AIDS (although a commute time of more than 45 minutes did negatively correlate with AIDS rate, suggesting that my original hypothesis had some degree of validity).

 Although all of the correlations I tried did not work out the way I had originally anticipated, the result often went to further my original hypothesis: that AIDS victims in Connecticut are more often poor, uneducated members of the African American and Hispanic populations than they are members of the gay community. Through the completion of my study of correlations between various economic, social, educational, and ethnic factors and the rate of AIDS cases in Fairfield County, I have added some proof to my original thesis and have shown that the data supports the argument that, in Fairfield County, the largest percentages of those infected with HIV/AIDS are not only unlike the typical AIDS patient represented in the media (i.e. a gay white male), but are also the victims of poverty, ignorance, language barriers, and drug use.