Conclusion-1

Conclusion-1 - factor among the three. Moreover, States...

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Conclusion By analyzing the data we collected, the first obvious fact we found is that gun rate per capita is significantly high compare to other factors (rate of unemployment and income), but the coefficient is very insignificant which means even though people who own guns may rob or kill someone easier than those who do not, but does not really matter here in our regression function. On the other hand, rate of unemployment does not seem to have much effect on the robbery cases; we were wrong about the influence cause by unemployment as people who loss jobs and in need of money may have intention to rob (coefficient = 0.000307902 very insignificant but does exist). Income is the most relevant
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Unformatted text preview: factor among the three. Moreover, States like California with a huge population have more robbery may be cause by environment as more intense job market competition. The regression function we got is fairly stable (fit), and explains about 40.8% of our data (R2 value), the error variances and the distribution of the error are also close to a normal distribution. After all, apparently, robbery is almost always about the money such as bank robbery and personal valuable belongings robbery; as we look into this by analyzing other possible factors we found that gun rate, income, and unemployment rate could also have some effect on robbery cases....
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This note was uploaded on 04/20/2010 for the course STAT 103 taught by Professor John during the Summer '09 term at UC Davis.

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