The table shows the values found in the error analysis. What method of forecasting would be best to use? What is the forecasted value?
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The table shows the values found in the error analysis. What method of
| MSE | forecast for week 13 | |
| Time Series | 1.08 | 14.76 |
| 3-week MA | 3.04 | 13.67 |
| exp smoothing | 11.04 | 9.5 |
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- Under what conditions might a firm use multiple forecasting methods?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 file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?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?
- The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?There is a bias towards under forecasting, though we cannot determine from the data provided whether that is severe bias or not O There is a severe bias towards under forecasting O Bias is negligible in this case Question 22 A MAD (Mean Absolute Deviation) of 17.3 suggests which of the following? O There is an over-forecasting bias of 17.3 O The standard deviation for the forecast data is 17.3 O All forecasts fall within 17.3 of the actual data O On average, the forecasts miss by 17.3 O Without knowing the average, the MAD is useless 0 日Week Sales1 27502 31003 32504 28005 29006 30507 33008 31009 295010 300011 320012 3150Develop a three-week moving average. A.What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) B.What is the value of MSE (mean squared error) with regard to a three-week moving average? (Make sure no decimal place and no comma. EX: 1234) C.Develop the exponential smoothing with the alpha value of 0.20. What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) D.What is the value of MSE (mean squared error) with regard to the exponential smoothing? (Make sure no decimal place and no comma. EX: 1234) E.Which method is more accurate? (1 for Moving average, 2 for Exponential Smoothing)
- 3)The errors in a particular forecast are as follows: 3, -3, 4, 0, -2. What is the tracking signal for each period of the forecast? (use excel to show formulas )Accuracy of forecasts. The manager of a large manufacturer of industrial pumps must choose betweentwo alternative forecasting techniques. Both techniques have been used to prepare forecasts for a sixmonth period. Using MAD as a criterion, which technique has the better performance record?FORECASTMonth Demand Technique 1 Technique 21 492 488 4952 470 484 4823 485 480 4784 493 490 4885 498 497 4926 492 493 493Plot the table and give at least three observations from the plot according to the given data Weeks Calls 3 month Weighted Moving Average Error =Abs (Actual -Forecasted) (Sum of Absolute percent error) 1 50 2 35 3 25 4 40 31.42857143 8.571428571 21.42857143 5 45 35 10 22.22222222 6 35 40.71428571 5.714285714 16.32653061 7 20 38.57142857 18.57142857 92.85714286 8 30 27.85714286 2.142857143 7.142857143 9 35 27.85714286 7.142857143 20.40816327 10 20 31.42857143 11.42857143 57.14285714 11 15 25.71428571 10.71428571 71.42857143 12 40 19.28571429 20.71428571 51.78571429 13 55 30 25 45.45454545 14 35 45 10 28.57142857 15 25 41.42857143 16.42857143 65.71428571 16 55 32.14285714 22.85714286 41.55844156 17 55 43.57142857 11.42857143 20.77922078 18 40 50.71428571…
