Tuesday, November 5, 2013

Small Town Utopia

          We all have our own biases about how the town that we live in is the best, but is there really a clear winner? That is the question I set out to answer in my project. Although there are many towns I could have put in my sample, I just used the small suburbs around Rochester. These suburbs included Byron, Kasson, Stewartville, Triton, and Pine Island. I chose six categories to rank each town on. Three of these categories were educationally based, and the other three were just having to do with the town. The six categories were MCA math scores, MCA reading scores, college readiness, average house value, houses per 1000 people, and crime rates. I chose these because I thought they were all pretty important to making a town great. I was hoping that I could collect numerical data for each category and using those numbers, rank each town. The academic data was the easiest to find. I was able to get that data easily all off of one website and then make graphs and compare them. The other three categories were a little bit more difficult because I had to find websites that had information for all of the towns on them so that I could be consistent. I found that certain websites had slightly different numbers for the categories, so if I used data for one town from one website, I needed to keep that same website for all of the other towns. I created graphs for all of the categories and used a z-score in order to rank the average house values.
            Although I thought it would be relatively easy to find all of the data and compare, it was harder than I thought. I think that the data that I chose provided a minimal undercoverage and nonresponse. This is because the categories that I chose weren't based off of surveys, it was strict city data. One bias that could have occurred was on the houses on the market. The website I used wasn't a specific real estate agency, but it could have been biased to one, making sure that more of those agencies' houses were showing up. I also learned that it was harder to rank the towns than I thought. It was easy when it came to academics because it was already in percent. I had to use z-scores and find the standard deviation for the house values, which is something I learned in stats. It was fun to be able to actually apply that to something that I was doing on my own. When looking at my graphs, it was interesting to see that the math test scores were a lot more varied than the reading test scores. All of the towns were in the 78%-88% when it came to the reading tests, while in math, the scores varied from 45%-74%. It makes me curious as to why it is that way, but that might be a project for another time. I learned that the area around Rochester is a pretty safe place when it comes to crime. All of the towns had the same crime rate, which was the same as the average crime rate of Minnesota. While looking at the amount of houses on the market, it made me wonder why certain towns would have more or less houses for sale. Is it because people are trying to get out? Is it because everyone is trying to move there, or no one wants to leave? Maybe there's a higher demand in some towns versus others. Again, that's not something I ever found out, but this project spurred more questions that I would sometime be interested in finding the answer to.

These are a couple of the graphs and the stats that I used for my project.

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