A correlation is a complementary, parallel, or reciprocal relationship, especially a structural, functional, or qualitative correspondence between two comparable entities[1]. It presents a different way of looking at data. I used the data from ESSAY ONE and I correlated it with other demographic data found on the census website. The purpose of the project was to see if things like race, poverty and education correlated with the total number of AIDS cases for that area. Based on my data for the various county subdivisions surrounding Trenton, New Jersey, I noticed that there were not many correlations. I observed that only three of my topics that I had chosen from the census website had a correlation with the total number of AIDS cases.

In order to decide if a correlation is present between the data, you need to first find the degree of freedom. Based on the handout that was given in class, the number of the degree of freedom is two minus the total number of samples that you are correlating. The next step is to find the correlation coefficient. The correlation coefficient is found by taking the percent of the demographic data and the total number of AIDS cases and putting it into a function in the Microsoft Excel worksheet. The next step is to find the confidence level. This number ranges from .10, to 0.1. The number calculates how sure a person is about the correlation.  Each decimal represents a different percent of confidence. For example, 0.10 means that a person is ninety percent confident that there is correlation between the data. The degree of freedom for my data is sixteen because I have eighteen samples. I tried to use a lot of the demographic information because I do not have many samples. To my surprise, nothing seemed to correlate. I think that no correlation existed because I have data for many different counties near Trenton and not for the cities close to Trenton like Ewing and Hamilton for example. Most of cities that I have data for are at least twenty or more minutes away from my house.