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.
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S. Department of Education; the Bureau of the Census, Department of Commerce and Geography Division; the Labor Department, Bureau of Labor Statistics; and the National Climatic Data Center, U.S. Department of Commerce. The data set was originally collected by Louis J. Moritz, an operations manager.” (Bowerman et. al., 2010). A copy of the available data set is attached in Appendix A. The data consists of the following information for each of the fifty states:
1. Property crime rate per hundred thousand inhabitants
2. Per capita income
3. High school dropout rate
4. Average precipitation in the major city
5. Percentage of public aid recipients
6. Population density
7. Public aid for families with children
8. Percentage of unemployed workers
9. Percentage of the residents living in urban areas
III. Analysis and methods
I used Minitab to analyze the given data and test the various facts and hypotheses about the data. I ran a multiple regression analysis on the data to determine which variables affected crime rate the most. In this scenario, our dependant variable was the crime rate for each state, and the independent variables were the other 8 variables (i.e., per capita income, dropout rate, etc.) given for each of the states. The Minitab output is shown in Appendix B and pertinent excerpts are shown below.
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Regression Analysis | | | | | |
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| R² | 0.686 | | | | |
| Adjusted R² | 0.625 | n | 50 | | |
| R | 0.828 | k | 8 | | |
| Std. 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 |
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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...
