Property Crimes

3432 WordsJun 14, 201214 Pages
CASE 49: PROPERTY CRIMES I. Executive summary The focus of this study is the examination of the data provided by U.S government agencies. Our analysis revealed that of the eight possible contributing factors, only three variables (namely, urbanization rate, high school dropout rate, and population density) affected property crime rates. Our data analysis model accounted for approximately 66% of the factors contributing to property crimes. The model is generally considered to be statistically strong, however, if we need to account for the remaining 34% of factors contributing to property crime rates in the U.S., further data and evaluation of other possible factors would be necessary. II. Introduction According to the US…show more content…
Error | 754.255 | Dep. Var. | CRIMES (Y) | | … | | | | | | | Regression output | | | | confidence interval | variables | coefficients | std. error | t (df=41) | p-value | 95% lower | 95% upper | Intercept | -1,008.0855 | 1,003.2571 | -1.005 | .3209 | -3,034.2043 | 1,018.0334 | PINCOME (X1) | 0.0156 | 0.0731 | 0.213 | .8323 | -0.1320 | 0.1632 | DROPOUT (X2) | 73.3997 | 21.5165 | 3.411 | .0015 | 29.9463 | 116.8532 | PUBAID (X3) | -49.3649 | 39.8547 | -1.239 | .2225 | -129.8531 | 31.1233 | DENSITY (X4) | -2.2108 | 0.7018 | -3.150 | .0030 | -3.6281 | -0.7934 | KIDS (X5) | 0.4108 | 1.3363 | 0.307 | .7601 | -2.2878 | 3.1095 | PRECIP (X6) | -0.5357 | 10.9622 | -0.049 | .9613 | -22.6744 | 21.6030 | UNEMPLOY (X7) | -57.4497 | 78.7026 | -0.730 | .4696 | -216.3928 | 101.4933 | URBAN (X8) | 65.8552 | 11.0268 | 5.972 | 4.74E-07 | 43.5862 | 88.1242 | | | | | | | | The summary of this analysis: 1. R^2 = 68.6%: This is the proportion of variation in the dependent variable Y that is explained by variation in the independent variables Xi. In other words, using this model, almost 67% of the variation in the crime rate can be attributed to the independent variables X1 – X8. 2. To determine how much effect each of the independent variables has on the dependent variable, we examine the correlation coefficient for each of the independent variables. The higher
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