The variables y = annual income (thousands of dollars), x1 = number of years of education, and x2 = number of years experience in job are measured for all the employees having city-funded jobs, in Knoxville, Tennessee. Suppose that the following regression equations and correlations apply:
i)  = 10 + 1.0x1, r = 0.30.
ii)  = 14 + 0.4x2, r = 0.60.
The correlation is -0.40 between x1 and x2. Which of the following statements are true and which are false?
a. The strongest sample association is between y and x2.
b. A standard deviation increase in education corresponds to a predicted increase of 0.3 standard deviations in income.
c. There is a 30% reduction in error in using education, instead of , to predict y.
d. When x1 is the predictor of y, the sum of squared residuals is larger than when x2 is the predictor of y.
e. If s = 8 for the model using x1 to predict y, then it is not unusual to observe an income of $100,000 for an employee who has 10 years of education.