4.33Storrs Cycles has just started selling the new Cyclonemountain bike, with monthly sales as shown in the table. First,co-owner Bob Day wants to forecast by exponential smoothingby initially setting February's forecast equal to January's saleswith a = .1. Co-owner Sherry Snyder wants to use a three-periodmoving average.%3DBOB'SERRORSHERRY'SERRORSALESBOBSHERRYJANUARY400FEBRUARY380400MARCH410APRIL375MAYa) Is there a strong linear trend in sales over time?b) Fill in the table with what Bob and Sherry each forecast forMay and the earlier months, as relevant.c) Assume that May's actual sales figure turns out to be 405.Complete the table's columns and then calculate the meanabsolute deviation for both Bob's and Sherry's methods.d) Based on these calculations, which method seems moreaccurate? PX

Using excel for performing regression with Sales as the dependent variable and period as independent…

a) Is there a strong linear trend in sales over time? b) Fill in the table with what Bob and Sherry each forecast fo May and the earlier months, as relevant. c) Assume that May's actual sales figure turns out to be 405 Complete the table's columns and then calculate the mear absolute deviation for both Bob’s and Sherry's methods. d) Based on these calculations, which method seems more accurate? Px

a) Is there a strong linear trend in sales over time? b) Fill in the table with what Bob and Sherry each forecast fo May and the earlier months, as relevant. c) Assume that May's actual sales figure turns out to be 405 Complete the table's columns and then calculate the mear absolute deviation for both Bob’s and Sherry's methods. d) Based on these calculations, which method seems more accurate? Px

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 26P: The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel...

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Similar questions

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_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_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_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?
12-1. The Hartley-Davis motorcycle dealer in the Minneapolis- St. Paul area wants to be able to forecast accurately the de- mand for the Roadhog Super motorcycle during the next month. From sales records, the dealer has accumulated the data in the following table for the past year. Month January February March April Мay June Motorcycle Sales 7 10 8 7 12 July August September 10 11 12 October 10 November December 14 16 a Compute a three-month moving average forecast of demand for April through January (of the next year). b. Compore a five-month moving average forecast for June drough January. c. Compare the two forecasts computed in parts (a) and (b) using MAD. Which one should the dealer use for January of dhe next year?
7. Using both time series methods of forecasting, predict the number of meals that should be prepared for the next Monday. The forecast for the most recent Monday was 367 and for the most recent Friday was 354. a=0.3 Show your calculations DAY Monday Tuesday Wednesday Thursday Friday WEEK 1 360 372 375 358 333 WEEK 2 375 381 373 368 377 WEEK 3 367 376 378 360 353 WEEK 4 373 380 376 363 370
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)