The Correlation Between Religious Population, Homeless Population and Homeless Youth Population

Transcription

1 Eleen Fernandez IB Precalculus Internal Assessment The Correlation Between Religious Population, Homeless Population and Homeless Youth Population Introduction: For my Internal Assessment, I am producing a correlation project that compares two variables to another. I am assessing the effect that the religious population in a city has on the percentage of people who are homeless, and further compare this to the percentage of the population who are homeless youth. I am going to accomplish this task by researching online the individual percentages of religious affiliation, homelessness, and homeless youth throughout the United States. Afterwards, I am going to set them up to compare against each other to see the correlation between these three variables. This is a topic I am interested in doing because I feel that it is an important and relevant issue in the modern day 21st century. I want to test if religious affiliation decreases both the homeless and homeless youth populations. Info/Measurements: Raw Data:

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5 used. See the sources section of my internal assessment for the links to all of the raw data Math Processes: The goal of my IA project is to compare the religious, homeless, and homeless youth populations of a state against each other. My hypothesis for this analysis of these three variables is that they will end up having a moderately strong correlation. I predict that with a

6 higher percentage of a religious population, there will be a lower percentage of homeless and homeless youth. Simple Math Processes:

7 Above, I have constructed a simple graph putting all of the variables against each other in an easy to read and orderly graph.the goal of this graph is to lay out the information in a spreadsheet to allow the viewer to compare and contrast with easy accessibility. This also helps with the main goal of the internal assessment, which is to compare these three variables with ease and test their correlation.

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9 In the three graphics above, I have constructed scatter plot graphs to compare each variable against another. This helps the viewer see the comparison in a more comprehensive manner, for they are able to more readily picture the direct correlation between each of the variables. Complex Math Process: In this last graph, I have laid out all three variables against each other in order to give a better perspective with the other graphs displayed above. Interpretation: In my math processes, I collected information to make two different kinds of simple math processes, as well as one complex. For my simple math processes, I made a graph comparing the percentages of the religious population, homeless population, and homeless youth population. These were all based on states in the United States of America. The first graph is fairly self explanatory, with each variable laid out in a simple layout. The goal of this graph was to directly compare each of the three variables directly against each other, in order

10 to truly understand the information collected. I used this first graph as a resource to base my other processes on. My second simple math process is composed of three separate graphs. They are scatter plot graphs, comparing each factor against the other. The titles of these graphs are Religious % by State v. Homeless % by state, Homeless % v. Homeless Youth %, and Religious % v. Homeless Youth %. For the first graph, it seems as if most of the homeless population percentage lies within the 1% and 2.5% marks on the graph. The higher percentages of homeless population lies between the 30% 40% range of religious belief. However, there does not seem to be much homelessness when the religious percentage is really low or, on the contrary, really high. The second graph of the three compares the homeless percentage by state by the homeless youth percentage by state. This graph looks like it has a moderately strong positive correlation. The third graph analyzes the percentage of religious affiliation in a state versus the percentage of homeless youth present. This graph is very similar to the first graph. There is no clear correlation, but the highest numbers of homeless youth tend to range between the 30% 40% religious percentage. It is mainly clustered in a constant line along the 1% and 2.5% ranges of homeless youth percentages. My final math process is a complex one. It is a correlation graph, comparing all three variables to each other in the form of a scatterplot graph. I chose to use shapes as point indicators in order to allow the graph to show the viewer where the variables overlapped. I used the religious percentage as the constant x variable, while the homeless and homeless youth percentages were matched up against each other as y values. These values overlap a lot, and only have a few values that don t quite match up. Even when these values do not match up, they typically are still quite close to one another on the graph. One can then analyze that in terms of homeless and homeless youth percentages in comparison to the religious percentages end up coming out as very similar values. When put into a GDC, the correlation of this graph is r, which essentially can be interpreted as no correlation whatsoever. Validity: As with every other correlation graph, there are limitations that my graphs and assignment possess. One of the main limitations is data, and the sheer availability of it. I only had 50 data points for each variable, which totals up to 150 data points I collected. However, this is not enough data for an even more thorough and accurate test to be done and the possible correlation of the religious, homeless, and homeless youth populations. Another limitation that I have identified is alternate factors causing the rise and fall of the homeless and homeless youth percentages. It could be that instead of religious population being the cause of these two variables influx or decrease, it instead was caused by an incredibly different factor. My hypothesis for the actual causation of these two variables is the amount of large cities in the state that the data was gathered in, as well as the population of each state. Other possible factors are economic statuses of the individual states, and the percentage of drug usage in each of these states.

11 Structure and Communication: My initial hypothesis of this internal assessment was that there was going to be a moderate to moderate high correlation between the three variables. However, my hypothesis was proven wrong, as the correlation of my graph amounted to zero, or no, correlation. If I were to repeat this project, I would choose a different subject matter, as well as do a χ 2 graph instead of a correlation graph. I would have also collected more data than I have presently done for this Internal Assessment project. Sources: The 2013 Annual Homeless Assessment Report (AHAR) to Congress. The U.S. Department of Housing and Urban Development. Office of Community Planning and Development. Web. < part1.pdf > Religion in the United States. Wikipedia. Web. < >