Estimate the regression model results. critically and consider how you would interpret the strengths and weaknesses of this model.

Model E: Burglary = f(PerCapIncome, %Bachelors, %Metro, Rain)

%UnemplRain %youngfemale 7.2 58.3 15.8% 7.7 13.6 16.1% 7.2 50.6 15.8% 8.9 22.2 16.0% 6.9 15.9 14.6% 7.8 50.3 14.6% 6.7 45.7 15.6% 7.2 54.5 14.5% 8.2 50.7 15.4% 6.1 18.9 16.0% 9 39.2 15.0% 7.6 41.7 16.1% 4.7 34 16.7% 5.3 28.9 16.4% 8 48.9 15.2% 6.7 60.1 16.0% 6.7 42.2 13.3% 6.6 44.5 14.2% 6.7 8.8 5 8.5 6.7 5.4 3.8 9.6 5.1 8.2 6.9 7 8 2.9 7.4 5.3 7.9 7.3 9.3 7.6 3.8 7.7 6.3 4.6 4.4 5.7 47.7 32.8 27.3 59 42.2 15.3 23.6 9.5 43.4 47.1 14.6 41.8 50.3 17.8 39.1 36.5 27.4 42.9 47.9 49.8 20.1 54.2 28.9 12.2 42.7 44.3 15.8% 16.0% 14.8% 16.6% 15.5% 15.3% 16.2% 14.3% 14.8% 13.6% 15.9% 15.3% 15.1% 19.3% 15.2% 16.2% 14.5% 15.4% 17.3% 15.6% 16.3% 15.1% 15.9% 19.3% 16.1% 15.0% SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA df SS MS F Significance F Regression Residual Total Coefficients Standard Error t Stat Intercept Rain % young female P-value Lower 95% 7 6.8 6.7 4.7 38.4 45.2 32.6 12.9 14.6% 14.6% 15.5% 15.7% Significance F Upper 95%Lower 95.0% Upper 95.0%