ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	24946.866	1	24946.866	4.502	.035b
Residual	892065.515	161	5540.780
Total	917012.381	162
a. Dependent Variable: Property Crime Rate per 1,000
b. Predictors: (Constant), Percent of Owner-Occupied Housing Units

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	76.348	11.736		6.505	<.001
Percent of Owner-Occupied Housing Units	-.785	.370	-.165	-2.122	.035
a. Dependent Variable: Property Crime Rate per 1,000

Research Hypothesis:

In this hypothesis, the independent variable (IV) is the level of neighborhood poverty. We are changing or manipulating this variable in our study. The idea is that neighborhoods with higher levels of poverty may have higher crime rates due to factors such as lack of resources, unemployment, and social disorganization.

The dependent variable (DV) is the rate of crime. We are interested in measuring this outcome. We want to see if changes in the level of neighborhood poverty affect the rate of crime.

The control variable (CV) is the level of education. This is a variable that could influence the relationship between the level of neighborhood poverty and the rate of crime. By controlling for this variable, we can better isolate the effect of neighborhood poverty on crime rates. For example, neighborhoods with higher levels of education might have lower crime rates, regardless of poverty level. By controlling education, we can see if poverty affects crime rates above and beyond the effect of education.