# Question

Refer to the previous exercise. MINITAB reports the results below for the multiple regression of y = crime rate on x1 = median income (in thousands of dollars) and x2 = urbanization.

Results of regression analysis

Correlations: crime, income, urbanization

a. Report the prediction equations relating crime rate to income at urbanization levels of (i) 0 and (ii) 100. Interpret.

b. For the bivariate model relating y = crime rate to x = income, MINITAB reports crime = -11.6 + 2.61 income Interpret the effect of income, according to the sign of its slope. How does this effect differ from the effect of income in the multiple regression equation?

c. The correlation matrix for these three variables is shown in the table. Use these correlations to explain why the income effect seems so different in the models in part a and part b.

d. Do these variables satisfy Simpson’s paradox? Explain.

Results of regression analysis

Correlations: crime, income, urbanization

a. Report the prediction equations relating crime rate to income at urbanization levels of (i) 0 and (ii) 100. Interpret.

b. For the bivariate model relating y = crime rate to x = income, MINITAB reports crime = -11.6 + 2.61 income Interpret the effect of income, according to the sign of its slope. How does this effect differ from the effect of income in the multiple regression equation?

c. The correlation matrix for these three variables is shown in the table. Use these correlations to explain why the income effect seems so different in the models in part a and part b.

d. Do these variables satisfy Simpson’s paradox? Explain.

## Answer to relevant Questions

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