t yt 1 59 2 68 3 64 4 64 5 56 6 61 7 63 8 59 9 59 10 58 11 67 12 66 13 68 14 77 15 67 16 75 17 77 18 75 19 74 20 68 The accompanying data file contains 20 observations for t and yt. Click here for the Excel Data File a. Use the simple exponential smoothing with a = 0.2 for making forecasts. Note: Do not round intermediate calculations. Round final answers to 2 decimal places. t Ут ŷt 1 59 - 2 68 3 64 20 20 68 b. Use the in-sample forecast errors to compute MSE, MAD, and MAPE. Note: Do not round intermediate calculations. Round final answers to 2 decimal places. Performance Measure MSE MAD MAPE (%) Value c. Make a forecast for period 21. Note: Do not round intermediate calculations. Round final answer to 2 decimal places. ŷ21

The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?

The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.

The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?