9. a. Obtain the linear trend equation for the following data on new checking accounts at Fair Sav Bank and use it to predict expected new checking accounts for periods 16 through 19. Period New Accounts Period New Accounts Period New Accounts 200 232 11 281 214 248 12 275 3. 211 8. 250 13 280 4. 228 253 14 288 235 10 267 15 310 b. Use trend-adjusted smoothing with a = .3 and B = .2 to smooth the new account data in par What is the forecast for period 16? %3D

9. a. Obtain the linear trend equation for the following data on new checking accounts at Fair Sav Bank and use it to predict expected new checking accounts for periods 16 through 19. Period New Accounts Period New Accounts Period New Accounts 200 232 11 281 214 248 12 275 3. 211 8. 250 13 280 4. 228 253 14 288 235 10 267 15 310 b. Use trend-adjusted smoothing with a = .3 and B = .2 to smooth the new account data in par What is the forecast for period 16? %3D

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 42P: The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars)...

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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 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_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?
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_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 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_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?
Explain the trade-off between responsiveness and stability in a forecasting system that uses time-series data. Who needs to be involved in preparing forecasts? 3. How has technology had an impact on forecasting? What capability would an organization have to have to not need forecasts? Give three examples of unethical conduct involving forecasting and the ethical principle each violates
Two different forecasting techniques (F1 and F2) were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows: PREDICTED DEMANDPeriod Demand F1 F21 68 66 662 75 68 683 70 72 704 74 71 725 69 72 746 72 70 767 80 71 788 78 74 80a. Compute MAD for each set of forecasts. Given your results, which forecast appears to be more accurate? Explain.b. Compute the MSE for each set of forecasts. Given your results, which forecast appears to be more accurate?c. In practice, either MAD or MSE would be employed to compute forecast errors. What factors might lead a manager to choose one rather than the other?d. Compute MAPE for each data set. Which forecast appears to be more accurate?
The following are the sales figures for 2018 through 2020 for a product. Data for ayear is available in two halves of the year, months January through June, and thenJuly through December (Half-year 2). Suppose we are interested in setting up a forecasting model using the exponentialsmoothing method for stationary processes with seasonality.a) Initialize the system based on the data provided for 2018 and 2019.b) Suppose the smoothing factors are ?= 0.1 and ?= 0.1. Forecast and update forthe first half of 2020 and then the second half of 2020 by incorporating the sales forthe first half of 2020 . Calculate the MAD based on 2020actuals and forecasts.c) Forecast sales for the two halves of 2021.