2010 HiMCM B题特等奖学生论文下载2791

2010 HiMCM B题特等奖学生论文下载2791

 

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论文摘要如下：

SUMMARY

 

A regional city has enlisted our help in curbing citywide gangs and violence. Before delving into solving the problem, our group established a set of assumptions. In order to compensate for population fluctuation, we normalized the data so that it would reflect incidents of violent crime per 100,000 people. We also agreed that the crime rate would be the dependent variable in our model and therefore not have an impact on the other variables. After setting some ground rules, we began constructing a mathematical model from which to draw conclusions on how to reduce violence. By organizing a linear regression, we were able to derive an equation that allows us to calculate the number of violent crimes per 100000 people in terms of the unemployment rate and the graduation rate. The variables were chosen because they exhibited the most statistical significance without overloading the equation with too many variables.

The next step was to research specifically how these two independent variables affected the dependent variable in real situations. We collected information from many different studies and articles to gain an understanding of the data and explain the statistical relationships between our variables. A particularly important discovery we made was that students who drop out of high school tend to become involved with gangs and violence. We also noted that in many states, the rate of violent crimes varies directly with the unemployment rate.

 

Bearing our research and model in mind, we have come to the conclusion that by forming tighter community bonds and providing extrinsic incentives for students, we can raise the graduation rate and thus quell gang violence or any other violent crimes. To lower the unemployment rate, we propose the institutionalization of vocational schools as an alternative option to public schools. We also sought to lower unemployment through the creation of public works programs. Our research shows that by keeping people employed, we can lower the number of violent crimes. To further exemplify these findings, we conducted a case study between our given city and the city of Dayton, Ohio.

 

Although our model can accurately predict the number of violent crimes per 100,000 people, it is contingent upon knowing the graduation rate and the unemployment rate. It is important to note that thus far the model is most useful for setting guidelines and goals to reduce the number of violent crimes and not projecting data into the future as is often the use of regression equations. However, with more research regarding future graduation and unemployment rates, our model could certainly be used to predict how many incidents of violence will occur. Assuming the success of our violence reduction programs, our model would prove that we can expect fewer violent crimes as a result of our efforts.

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