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Suppose a data set of 177 observations (N = 177) was analyzed using OLS to examine the factors influencing the salary of CEOs. The regression results are as follows, with standard errors in parentheses: Isalary = 4.7 +0.15 Isales +0.10 Imktval +0.010 ceoten (0.35) (0.06) (0.04) (0.003) where Isalary = log of salary, measured in thousands of dollars Lsales = log of sales, with sales measured in millions of dollars Imktval = log of market value of firm, with market value measured in millions of dollars ceoten = years as CEO at current firm n = 177 R = 0.331 QUO = n-1 - exp (:) = 1.35 The predicted log of salary for a CEO of a firm with $7 billion in sales (or $7,000 million) and a $12 billion market value (or $12,000 million), who has been the CEO for 10 years, is 2,254.8 7.774 ) 7.0673 8.4808 6.3606 Suppose you would like the predicted salary, and not the predicted log salary, for a CEO of a firm with $7 billion in sales (or $7,000 million) and a $12,000 billion market value (or $12 million), who has been the CEO for 10 years. The predicted salary for a CEO in this scenario would be thousand. $1,583.54 il $1,173.00 $1.58 $1,425.19 $1.35