**Correlations**

**My studies on AIDS
epidemic in Buffalo have brought me to analyze the relationships of dependent
variable present in correlations. This allows a new perspective into the study
of AIDS with the use of correlations coefficients determine the direction and
strength of the relationship. The coefficient ranges from -1 to +1.With a
negative correlation (-1) the relationship is inverse meaning that as one
dependent increases the other decrease. A positive correlation means the
relationship is direct since as one increases the other also increases. To find
the correlation coefficient a covariance is used to show the degree which the
variables diverge together. For my studies my covariance was .29 for my 46 zip
codes. The dependent variables I correlated with the rate variables included
public assistance, income, race, property ownership, foreign born population and
gross rent. **

** I correlated public
assistance and income with the AIDS rates because I wanted to see if there might
be a relationship between rates of AIDS. I expected the correlation of public
assistance and the AIDS rate to be inverse because I though that the more
assistance the better of they were since they had to means to protect
themselves. That was not the case since the graph produced a positive
correlation 0.76 that shows that the more public assistance received, the more
AIDS. (Table 1) This relationship is probably due to the fact the people
receiving public assistance in their later stage of the disease are so weak that
they need this assistance. Furthermore, those on public assistance for other
reasons other than AIDS are not working and left with plenty of free time to
acquire or transmit HIV. When I correlated the median income I had similar
expectations as the public assistance and this time I was right. The correlation
produced a negative relationship of -0.71 that showed that the more income
received the less AIDS. (Table 2) With more money the people have the means to
protect themselves and find the infection of HIV sooner in order to get
treatment. Maps I**

** To further my study I
correlated the AIDS rates with race. I though that it would be interesting to
see how the AIDS rate connected with the various relationship. The races I
correlated included Whites, Blacks and Hispanics. I expected the relationship to
be direct for all cases because I perceive the AIDS epidemic as an equal
opportunity killer. That was not the case since the white population showed a
negative correlation while the Blacks and Hispanics had positive correlations.
The White population’s negative correlation of -0.73 shows that as the
population grows, the AIDS rate decrease. Among the Blacks the correlation was
positive with 0.66 and among the Hispanics it was 0.52, showing that the AIDS
rates increase with the increase in both populations. (Table 3,
Table 4,
Table 5)The
correlations show that the white population is identifying AIDS and tackling it
with education, while the Blacks and Hispanics fail face the AIDS epidemic. Most
of the white population lives in the suburban areas while the Black and
Hispanics are concentrated in the close quarter in the inner city. This explains
why the few media coverage of the AIDS epidemic in Buffalo calls upon the Blacks
and Hispanics to get tested. Another group I chose to investigate was the Indian
population. I was not surprised to learn that they fell in the same category of
the Blacks and Hispanics. The group had the positive correlation of 0.30. (Table
6)In their smaller clusters outside of the city the Indians my not be able to
reach centers HIV/AIDS testing. Maps II**

** Other dependent
variables I observed were the median gross rent for the white population and the
median value for all owner-occupied housing units. I was attracted to these
variables because I wanted to see how people with financial stability of home
correlated with the AIDS rates. I expected that the higher the gross rent the
more valuable the property, which would suggest that the inhabitants had money.
Since the higher income produced a negative correlation I expected the gross
rent and property value would have the same effect. My hypothesis was correct
since inverse relationships were observed in both variables. As the median gross
rent increases the AIDS rates decreases, producing a negative correlation of
-0.56. (Table 7) The median value for all owner-occupied housing units also held
an inverse relationship with the AIDS rates and had a negative correlation of
-0.43. (Table 8) The results really show that wealth has a great impact on the
AIDS epidemic. People with the means are able to engage in the fight against
AIDS while the impoverished people slowly die off. **

** Moreover I
correlated the foreign origins of various people to the AIDS rates. I did so
because I am aware that Buffalo, New York is the destination point of may
immigrants, I can include myself in that count. I look at the foreigners from
Europe, Africa and the Americas. I expected the correlations to produce a direct
relationship with Africa because I am aware that it is third world but was a bit
uncertain with Europe and the Americas which are more developed. My speculations
about foreigners from Africa were correct since a positive relationship with the
AIDS rates was produced with a positive correlation of 0.35. (Table 9)The
correlation shows that the more African immigrants the increase of AIDS rate,
the same can be said of the foreigners from the Americas that produced a
correlation of 0.44. (Table 10)I was surprised to learn that the Europeans
foreigners had an inverse relationship with the negative correlation of -.61.
(Table 11) The graph shows that as more Europeans emerged from Europe the lower
the AIDS rates. **

**
In all my studies
through correlations have offered a new way to perceive the AIDS epidemic in
Buffalo. Through my correlations of races I was able see that Blacks and
Hispanics are the greatest risk. This group of people that are either not
receiving enough information or unable to access the aid. My correlations also
proved that the AIDS rates are the highest among the poor in the projects and
ghettos which mainly consist of minorities. **

**Maps**