Concept explainers
Townside Food Vending operates vending machines in office buildings, the airport, bus stations, colleges, and other businesses and agencies around town and operates vending trucks for building and construction sites. The company believes its sandwich sales follow a seasonal pattern. It has accumulated the following data for sandwich sales per season during the past four years.
Develop a seasonally adjusted
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- Under what conditions might a firm use multiple forecasting methods?arrow_forwardThe 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?arrow_forwardThe 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.arrow_forward
- 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?arrow_forwardThe 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?arrow_forwardThe accompanying dataset provides data on the monthly usage of natural gas (in millions of cubic feet) for a certain region over two years. Implement the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-26, where x = 0.6 and y=0.9. Then find the MAD for periods 13-24. Click the icon to view the monthly use of natural gas data. Use the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-18, periods 19-24, and then for periods 25 and 26. (Type integers or decimals rounded to two decimal places as needed.) Period Forecast 13 14 15 16 17 18 C... ☐☐☐☐☐☐ Natural Gas Usage Month Period Gas Usage 1 2 3 4 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 5 6 TORINESTRANN 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 251 227 157 138 53 33 30 29 28 41 87 196 231 252 240 135 34 34 27 27 29 39 86 188 - Xarrow_forward
- The accompanying dataset provides data on the monthly usage of natural gas (in millions of cubic feet) for a certain region over two years. Implement the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-26, where x = 0.5 and y = 0.8. Then find the MAD for periods 13-24. Click the icon to view the monthly use of natural gas data. Use the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-18, periods 19-24, and then for periods 25 and 26. (Type integers or decimals rounded to two decimal places as needed.) Forecast Period 13 14 15 16 17 18 Natural Gas Usage Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Period Gas Usage 1 2 8699 A WI 3 4 5 7 9 10 11 12 13 14 1516718192021 22 23 24 241 221 155 137 54 34 29 27 30 40 90 201 229 238 242 136 34 34 28 26 27 40 86 187arrow_forwardUse the trend projection method, and the trend projection with seasonal adjustment method to create forecasting models in Excel. Next, using the two models, compute the forecasted values of monthly total passengers between 2010 to 2012 Compare the above two models using MAD, MSE, and MAPE Please explain which of the two models is performing better and why? Use the best model to forecast the monthly total passengers for year 2013arrow_forwardDiscribe how to determine an appropriate method for calculating the forecast error and discuss the ideal method for long range forecasting.arrow_forward
- State the assumptions made when using a time series forecasting techniques ?arrow_forwardDiscuss when to use a time series forecasting techniques ?arrow_forwardThe data shown in the following table represent visitors to the Hawaiian Islands over the past several years, by quarter. Use the data for the first five years to estimate the seasonal and trend factors. Then build an exponential smoothing model (incorporating both trend and seasonal factors) to provide for the forecasts of the remaining periods. Plot the actual visitors and the forecasts. Compare the accuracy of the forecasts in the 1985 to 1990 period with those subsequent to that period. Thousands of Persons Second Third Fourth Annual First Quarter Year Quarter Quarter Quarter Visitors 1980 1,004.8 942.9 1,047.3 939.5 3,934.5 1981 959.0 984.5 1,042.3 948.8 3,934.6 1982 1,054.0 1,048.7 1,110.7 1,029.5 4,242.9 1983 1,069.9 1,071.5 1,146.4 1,080.2 4,368.0 1984 1,218.5 1,206.8 1,222.9 1,207.4 4,855.6 1985 1,301.5 1,129.6 1,266.9 1,186.2 4,884.2 1986 1,393.2 1,421.2 1,450.9 1,341.8 5,607.1 1987 1,448.9 1,370.0 1,555.2 1,425.8 5,799.9 1988 1,484.9 1,488.2 1,635.6 1,533.7 6,142.4 1989…arrow_forward
- Practical Management ScienceOperations ManagementISBN:9781337406659Author:WINSTON, Wayne L.Publisher:Cengage,Contemporary MarketingMarketingISBN:9780357033777Author:Louis E. Boone, David L. KurtzPublisher:Cengage LearningMarketingMarketingISBN:9780357033791Author:Pride, William MPublisher:South Western Educational Publishing