1) Interpret the results of the regression analysis between home ownership and crime, as we learned in class. 2) What is the predicted violent crime rate per 100,000 population for a city with 72.2% home ownership? Show your work. 3) List at least two city-level independent variables (other than home ownership) that might influence a city's crime rate. Explain why those X's could influence crime.

Correlations owner vio_rate owner Pearson Correlation -.163 Sig. (2-tailed 518 18 18 vio rate Pearson Correlation -.163 Sig. (2-tailed) .518 18 18 ANOVA Model Sum of Squares df Mean Square Sig. Regression 21329.391 21329.391. .518 Residual 781450.618 16 48840.664 Total 802780.010 17 Regression a. Dependent Variable: vio_rate b. Predictors: (Constant), owner Coefficients" Standardized Variables Entered/Removed" Model Unstandardized Coefficients Coefficients Sig. Model Variables Entered Variables Removed Method B Std. Error Beta owner Enter (Constant) 631.380 427.713 1.476 .159 a. Dependent Variable: vio_rate owner 4.386 6.638 b. All requested variables entered -.163 -.661 .518 a. Dependent Variable: vio rate Model Summary Model Std. Error of the R R Square Adjusted R Square Estimate .163" .027 ..034 - . .' 220.99924 a. Predictors: (Constant), owner .e