ANOVA Sum of Squares Model df Mean Square Sig. Regression 1922090.32 Residual 12

Question

ANOVA Sum of Squares Model df Mean Square Sig. Regression 1922090.32 Residual 1274016.18 Total 3 640696.773 23.133 000b 46 27696.004 49 3196106.50 a. Dependent Variable: Burglary Rate per 100K b. Predictors: (Constano, State in South, Percent1 8to24, Percent Individuals below poverty Coefficients Standardized Model Std. Error 134.757 385.337 9.099 Beta (Constant Percent Individuals below poverty Percent18to24 State in South 350 518 4.729 43.033 -14.076 51.773 197.18359.308 -.025-272 364 3.325 Coefficients 95.0% Confidence Interval for B Correlations Model Sig Lower Bound Upper Bound Zero-order Partial 728-640.886 910.401 61.349 90.138 Percent Individuals below 000 24.717 .710 .572 Percent1 8to24 State in South 787 118.289 -.072-.040 002 77.803 316.563 637 440 Coefficients Correlation Model Part (Constant) Percent Individuals below 440 poverty Percent1 8to24 State in South -.025 309 a. Dependent Variable: Burglary Rate per 100K Page 2

Explanation / Answer

a)To find the degree of predictability of a variable explaining a dependent variable, once the t-test gives a normal p-value meaning the null hypothesis is rejected, we check for R coefficient for a variable. The variable with the highest value of R coefficient explains the most of the dependednt variable as it is a measure of -how much standard deviation in the dependednt variable is explained by the independent variable. We can see that percentage individual below poverty has the highest correlation with the dependent variable and thus that is the strongest predictor. The second strongest predictor is the state in the south.

b) The percentage of explanation is checked by the R coefficient. Thus 77.5% is explained by all the three variables.

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