Exponential Smoothing Forecasting Use the area below to draw a line chart for single exponential smoothing forecast. Smoothing constant 0.2 Time Observation Forecast Error Error^2 Formulas 5 Period 87 #N/A V#N/A I #N/A * #N/A V #N/A V #N/A V #N/A * #N/A * #N/A V #N/A * #N/A V #N/A 2 #N/A 93 88 89 #N/A 3 #N/A V #N/A 8. #N/A * #N/A * #N/A * #N/A 9 4 #N/A 10 97 #N/A 11 6. 86 #N/A 12 7 91 #N/A *#N/A * #N/A T #N/A #NIA v #NIA 8 89 92 13 #N/A 14 #N/A 93 89 94 15 10 #N/A 16 11 #N/A 17 12 #N/A 18 Next period forecast: 13 #N/A 19 20 MSE #N/A 21 22
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- Under what conditions might a firm use multiple forecasting methods?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 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 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_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 table shows the values found in the error analysis. What method of forecasting would be best to use? What is the forecasted value? MSE forecast for week 13 Time Series 1.08 14.76 3-week MA 3.04 13.67 exp smoothing 11.04 9.5 Group of answer choices A.) Time Series, forecasted value = 14.76 B.) 3-week MA, forecasted value = 13.67 C.) exp smoothing, forecasted value = 9.5 D.) Time Series, forecasted value = 1.08 E.) 3-week MA, forecasted value = 3.04 F.) exp smoothing, forecasted value = 11.04Q5) Monthly sales for a six month period are as follows: Month SalesJan 18,000Feb 22,000Mar 16,000Apr 18,000May 20,000June 24,000 Compute the sales forecast for July using the following approaches: (1) Four-month moving average; (2) Weighted three-month moving average using .50 for June, .30 for May and .20 for April;(3) Exponential smoothing with α (smoothing constant) equal to .40, assuming a February forecast of 18,00014.5. For the data in Exercise 14.4, use an a of 0.1 to make a forecast for July F Ft-1 + 0.1(At-1-Ft-1) Months January 15 February 18 March 22 23 27 26 April May June Demand Smoothing unit (At) (St) July 15 15.3 15.97 16.67 17.7 18.53 Forecast for July= 18.53 Forecast (Ft) 15 15.3 15.97 16.67 17.7 18.53
- Given November actual demand of 61, November forecast of 58, and an alpha of 0.4, November trend value +1.03, Beta of 0.3 what would the forecast including trend (FIT) for the December period be using exponential smoothing model 2? Select one: a. 62.03 b. cannot be found c. 68.76 d. 65.72Accuracy 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 493Week 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)