My study examined data provided various federal agencies pertaining to crime rates in all fifty
U.S. states and eight possibly related factors such as per capita income, high school dropout rate,
public aid status, population density, number of kids, average precipitation, unemployment rate and
urban setting. My analysis revealed that of the eight possible factors, only three variables, urbanization
rate, high school dropout rate and population density affected property crime rates. My data
demonstrated my analysis model accounted for 66% of the factors contributing to property crimes.
The model is considered moderately strong in demonstrating a correlation ...view middle of the document...
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 was originally collected
By Louis J. Moritz, an operation manager.(Bowerman et al 2010) A copy of the data set is 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.
5. Percentage of public aid recipients.
6. Population density.
7. Percentage of unemployed workers.
8. Percentage of urbanization.
III. Analysis and methods
I used Minitab version 16 to analyze the data and test and enumerate the various correlations and
hypotheses about the data. I ran a multiple regression analysis to determine which variables had the
highest correlation to property crime rates. I used crime rate as the dependent variable (Y) and the
eight other variables were the independent variables (X). The Minitab 16 output is shown in
Appendix B. Important parts of the data are submitted below:
R-Sq=69.5% or .695
r is therefore .8336666
Predictor Coef SE Coef T P
Constant -1137 1226 -0.93 0.359
STATE 6.116 8.238 0.74 0.462
PINCOME -0.00221 0.08071 -0.03 0.978
DROPOUT 82.01 22.13 3.71 0.001
PUBAID -117.11 79.27 -1.48 0.147
DENSITY -2.0774 0.7446 -2.79 0.008
KIDS 1.012 1.462 0.69 0.493
PRECIP 2.20 11.26 0.20 0.846
UNEMPLOY -35.26 81.48 -0.43 0.668
URBAN 65.19 11.04 5.90 0.000
The summary of this analysis:
1. R2=69.5%: This the proportion of variation in the dependent
variable Y that is correlated to the independent variables Xi.
Using this model 69.5% of the variation in crime rate is
correlated to the independent variables X1-X8.
2. To determine how much each variable is correlated to the
dependent variable Y crime rate I examined the correlation
coefficient for each X
variable a positive coefficient value indicates that the variable
has positive effect on the dependent variable Y. The output
shows that dropout rate has the highest correlation at 82.01,
followed by urbanization with a correlation coefficient of 65.19
and public aid with a -117.11 coefficient and unemployment with
a coefficient of -35.26.
3. Next I examined the p-value of the data using an alpha=0.1.
Dropout, density and urban have p-values less than .1. Urbanization
A p=value of .000 which indicates urbanization has the highest
correlation with property crime rates.