1. In Table 9.16 in Problem 9.24 regarding Flonda counties, refer to the vanables Y = crime rate (number per 1000 residents). X) = median income (thousands of dollars), and X2 = percent in urban envirorument

a) Figure 11.12 shows a SAS scatter diagram relaung Y to X, Predict the sign that the estimated effect of X, has in the prediction equation = a + bx. Explain

b) Figure 11.13 shows a SAS partial regression plor relating Y to X, controlling for X2. Predict the sign that the estimated effect of X, has in the prediction equation a+biX1+b2X2. Explain =

c) Table 11.10 shows part of a SAS printout for the bivariate and multiple regression mod- els. Report the prediction equation relating Y to X1, and interpret the slope.

d) Report the prediction equation iclating Y to both X1 and X2. Interpret the coefficient of X), and compare to (c).

e) The Pearson correlations are ryx, 43. rx = .68,7x, x = .73. Use these to ex- plain why the X, effect seems so different in

(c) and (d).

f) Report the prediction equations relating crime rate to income at urbanization levels of (i) 0. (n) 50. (iii) 100. Interpret