\fA trucking company wants to predict the yearly maintenance expense (Y) for a truck using the number of miles driven during the year (X1) and the age of the truck (X2, in years) at the beginning of the year. The company has gathered the data given in the le P10_16.xlsx, where each observation corresponds to a particular truck. a. Estimate a multiple regression equation using the given data. Round your answers to three decimal places, if necessary. V: 9+ 0X1+ 0X2 Interpret each of the estimated regression coefcients. Round your answers to the nearest whole dollar, if necessary. Holding all else constant, the yearly maintenance expense Q by approximately $ 0 when the number of miles driven during the year rises by 1,000. Holding all else constant, an additional year in the age of the truck results in approximately 1; 0 0 in the yearly maintenance expense. Why is the magnitude of the Miles Driven coefficient so much lower than the magnitude of the Age of Truck coefcient? Is it because Miles Driven is not as important in predicting Maintenance Expense? 9, eeeeeee the {we 9 every 9 meeeeeeee b. Interpret the standard error of estimate Se for these data. Round your answer to the nearest whole dollar, if necessary. About 9 of the predictions of the yearly maintenance expense v 9, made by the model, will be yearlym ntenanceexpense v Q. Interpret R2 for these data. Round your answer to the nearest whole number, if necessary. The model explains o \"In of the variation of the yearly maintenanoe expense v 9. @ $ 0 of the actual