A firm uses simple exponential smoothing with trend adjustment to forecast demand, "a = 0.6" and "B=0.6". The Forecast including Trend (FIT) for the 1st month of 2022 was 434 units whereas the actual demand turned out to be 488 units, the trend over that period was 26. Calculate the demand forecast including trend for the 2nd month.
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- 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_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 file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. 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?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?
- Under what conditions might a firm use multiple forecasting methods?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?Herman Hahn is attempting to set up an integrated forecasting and inventory controlsystem for his hardware store, Hahn’s Hardware. When Herman indicates that outdoorlights are a seasonal item on the computer, he is prompted by the program to input theseasonal factors by quarter.Unfortunately, Herman has not kept any historical data, but he estimates that firstquarter demand for the lights is about 30 percent below average, the second-quarterdemand about 20 percent below average, third-quarter demand about average, andfourth-quarter demand about 50 percent above average. What should he input for theseasonal factors?
- 1. Using MAD as the criterion, which of the following models would you use for thegiven time series data? Why?A. Naïve approach;B. 5-month SMA model;C. WMA model with weights 0.1, 0.3, and 0.6; orD. ES model with α = 0.5 and a forecast of 3,500 liters in the first month.NOTE: In answering Item 1, mention the whole description of the model; i.e., not just“SMA model”, but “SMA model with n = ...”; not just “WMA model”, but “WMA modelwith weights ...”; not just “ES model”, but “ES model with α = ...”.Please do not give solution in image format thanku supose that the demand in period 1 was 7 units and the demand in period 2 was 9units . Assume that the forecast for period 1 was for 5 units .If the firm uses exponential smoothing with an alpha value of .20. what should be the forecast for period 3Simple exponential smoothing (with a 0.2) is beingused to forecast monthly beer sales at Gordon’s LiquorStore. After observing April’s demand, the predicted demandfor May is 4,000 cans of beer.a At the beginning of May, what is the prediction forJuly’s beer sales?b Actual demand during May and June is as follows:May, 4,500 cans of beer; June, 3,500 cans of beer. Afterobserving June’s demand, what is the forecast for July’sdemand?c The demand during May and June averages out to 4,5002 3,500 4,000 cans per month. This is the same asthe forecast for monthly sales before we observed theMay and June data. Yet after observing the May andJune demands for beer, our forecast for July demand hasdecreased from what it was at the end of April. Why?
