Correlations  

             In my first impressions paper, I found out that Rahway, New Jersey’s cumulative rate average of 819, which concludes that Rahway’s average is more than four times the national average. For such a small town, with a population barely over 27,000, I was surprised that the rate would exceed 800. These high rates are usually found in cities larger than Rahway, especially with a rate that is unraveling exponentially. I did not expect my hometown to have such high numbers of AIDS cases, but after looking at the statistics of the disease, for the male and female rates, my eyes were widened of the defects of the town and the destructive path it’s headed for.

            On the other hand, it was interesting to see the correlations within sixteen cities in New Jersey. The Pearson correlation is what I used to help me determine sufficient relationships between different factors. The p-value that determines an adequate correlation has a correlation coefficient of 0.497, leaving a five percent chance for marginal error or uncertainty. Any coefficient greater than the accepted p-value, either negative or positive, is considered to be a sufficient correlation. These 16 cities that I choose to correlate will cover my hometown, Rahway, NJ, cities in Union County, and larger cities that are within 25 miles of the county.

Cities Covered in Analysis

A correlation that I found to be very interesting was the comparison between the median family incomes and the cumulative average of AIDS. Just as I presumed, there was a sufficient correlation between the two variables, which was -0.7105, meaning that they effect one another in many cases. Since I found that there was a significant correlation, I decided to find a correlation within the cumulative rate average of AIDS and the median family incomes for Blacks (only) and Hispanics (only), which effectively, turned out to be very sufficient. My correlation coefficient for the Blacks (only) [Figure 2] came out to be strong a -0.5453 and the correlation with Hispanics (only) [Figure 3] was -0.7804, implicating that there is a relationship between the cumulative rate of AIDS and the median income for both groups. My data suggests that there was a stronger correlation between Hispanics (only) than the Blacks (only) in New Jersey. This was a shock to me because I thought there was going to be stronger correlation for the Blacks (only).

Significant Correlations with Cumulative AIDS Rate

Correlation Figures of Median Income vs. Cumulative Rate of AIDS

    I chose these correlations because I thought there was going to be a relevant correlation between lower income families and the AIDS rate in a particular group, seemingly, I was successful. Statistics show that African-Americans and Hispanics are more prevalent to have AIDS infections in their communities. To prove to that point, the concentrated an area is with African-Americans and Hispanics, the more common it is to see higher HIV/AIDS rate in that region.

The county seat in Union County is Elizabeth, one of the largest cities in New Jersey. According to my calculations and correlations, Elizabeth unexpectedly has a rate nearly six times the nation average. The populations of Hispanics and African-Americans are very high in this area, which culminate the high numbers of AIDS. With a  population of 120,568 residents, Elizabeth as an average rate of AIDS case of 1.2 out of 100 people the infection. Elizabeth’s demographic population is predominantly Hispanics, but has a huge concentration of both of these particular groups in one area.

Elizabeth’s Population for Hispanics and African-Americans

Many of the variables that I presumed were going to correlate to the AIDS rates did not come out to be so productive. I picked factors consisting of the level of educational attainment for certain groups and age groups that I thought would influence the rate of AIDS, but resulted in a negative speculation. I took the percentage of Whites (only) and Blacks (only) in educational attainment from grades nine through twelve (who did not receive a high school diploma) in comparison to the cumulative rate average of AIDS. I assumed that the students enrolled in college (undergraduate) would impact the rate of AIDS. However, the correlation coefficient, between the two factors, yielded 0.3563 for the Whites (only) [Figure 5] and -0.3595 for Blacks (only). My data suggests that college students are not significantly the cause for the rates of AIDS because my correlation coefficients were not impeccably satisfactory.

With four different college campuses among these cities, which include state universities and community colleges, I was curious about the correlation of the percentage of college students in the different cities and those who were over twenty-five years of age and numbers of how many college students have AIDS in this area, especially with a high rate of minorities. Based on the correlations that I calculated, certain factors, like the median family has affected the cumulative rate average of AIDS. With all this information, many of my guesses and assumptions were incorrect. I have realized that my community and the areas around it do have a critical problem with the HIV/AIDS infection, and prevention is necessary immediately.