George Kyparisis owns a company that manufactures sailboats. Actual demand for George's sailboats during each of the past four seasons was as follows:SeasonYear1234Winter1,4001,2801,080960Spring1,5601,4001,6401,500SummerFall1,0202,1002,0402,000600770690560George has forecasted that annual demand for his sailboats in year 5 will equal 6,500 sailboats.Based on the given data and using the seasonal model, the demand level for George's sailboats in the spring of year 5 will be 1810 sailboats (enter your final answer as a whole number andround all intermediate calculations to two decimal places).

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Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 40P: The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels....

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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?
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?
Sales of Volkswagen's popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Using exponential smoothing with a smoothing constant (α) of 0.30 and a starting forecast of 415.00, the following sales forecast has been developed: Year Sales Forecasted Sales 2005 455 415.00 2006 502 427.00 2007 518 449.50 2008 563 470.05 2009 584 497.94 Part 2 The MAD for a forecast developed using exponential smoothing with α = 0.30 is enter your response here sales (round your response to two decimal places). Part 3 Forecasted sales for years 2006 through 2010 using exponential smoothing with α = 0.60 and a starting forecast of 415.00 are (round your responses to two decimal places): Year 2005 2006 2007 2008 2009 2010 Forecasted Sales 415.00 enter your response here enter…
Calanute Beach Resort, a fictional seaside luxury hotelin Goa, India, had the following occupancy rates for 12months in 2014Month Occupancy Rate in %1 652 683 724 755 786 837 928 889 7610 6511 6412 69a Forecast the occupancy rate for January2015 usingsimple exponential smoothing with α = 0.4. Assumethat the forecast for Month 2 (F2) is 65%. b Forecast the January 2015 occupancy rate usingtrend-adjusted simple exponential smoothing with α =…
George Kyparisis owns a company that manufactures sailboats. Actual demand for George's sailboats during each of the past four seasons was as follows: Season Winter Spring Summer Fall 1 1,440 1,560 1,040 640 2 1,280 1,440 2,120 750 Year 3 1,080 1,620 2,020 650 4 920 1,500 1,960 560 George has forecasted that annual demand for his sailboats in year 5 will equal 6,000 sailboats. Based on the given data and using the seasonal model, the demand level for George's sailboats in the spring of year 5 will be sailboats (enter your final answer as a whole number and round all intermediate calculations to two decimal places). ib the ng a rom E
Income at the architectural firm of Spraggins and Yunes for the period February to July was as follows: Month Income ($000's) February March 70.0 68.5 The mean squared error (MSE) for the forecast developed using trend-adjusted exponential smoothing = April 64.8 May 71.7 June 71.3 July 72.8 Assume that the initial forecast for February is 65.0 (in $ thousands) and the initial trend adjustment is 0. The smoothing constants selected are α = 0.10 and ß = 0.2. Using trend-adjusted exponential smoothing, the forecast for the architectural firm's August income = thousand dollars (round your response to two decimal places). (thousand dollars)² (round your response to two decimal places).
Additional Algo 15-12 Forecasting with Seasonality Demand history for the past three years is shown below, along with the seasonal indices for each quarter. Year Year 1 Year 2 Year 3 Quarter: 580338888888 Demand 796 748 493 437 723 758 Year 4, Q1 forecast 538 467 734 735 501 466 Seasonal Index 1.218 1.231 0.829 0.741 1.218 1.231 0.829 0.741 1.218 1.231 0.829 0.741 Use exponential smoothing with alpha (a) = 0.15 and an initial forecast of 616 along with seasonality to calculate the Year 4, Q1 forecast Note: Do not round intermediate calculations. Round your answer to the nearest whole number.
George Kyparisis owns a company that manufac-tures sailboats. Actual demand for George’s sailboats duringeach of the past four seasons was as follows: George has forecasted that annual demand for his sailboats inyear 5 will equal 5,600 sailboats. Based on this data and themultiplicative seasonal model, what will the demand level befor George’s sailboats in the spring of year 5?
Sales of Volkswagen's popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Using exponential smoothing with a smoothing constant (a) of 0.30 and a starting forecast of 415.00, the following sales forecast has been developed: Forecasted Year Sales Sales 2005 455 415.00 2006 510 427.00 2007 516 451.90 2008 570 471.13 2009 585 500.79 The MAD for a forecast developed using exponential smoothing with a = 0.30 is 74.04 sales (round your response to two decimal places). Forecasted sales for years 2006 through 2010 using exponential smoothing with a = 0.60 and a starting forecast of 415.00 are (round your responses to two decimal places): Year 2005 2006 2007 2008 2009 2010 Forecasted Sales 415.00 439 481.6 502.24 542.9 568.12 The MAD for a forecast developed using exponential smoothing with a = 0.60 is decimal places). sales (round your response to two
Sales of Volkswagen's popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Using exponential smoothing with a smoothing constant (a) of 0.30 and a starting forecast of 415.00, the following sales forecast has been developed: Forecasted Year Sales Sales 2005 455 415.00 2006 510 427.00 2007 516 451.90 2008 570 471.13 2009 585 500.79 The MAD for a forecast developed using exponential smoothing with a = 0.30 is sales (round your response to two decimal places).
Sales of Volkswagen's popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Using exponential smoothing with a smoothing constant (a) of 0.30 and a starting forecast of 415.00, the following sales forecast has been developed: Forecasted Year Sales Sales 2005 455 415.00 2006 510 427.00 2007 516 451.90 2008 570 471.13 2009 585 500.79 The MAD for a forecast developed using exponential smoothing with a = 0.30 is 74.04 sales (round your response to two decimal places). Forecasted sales for years 2006 through 2010 using exponential smoothing with a = 0.60 and a starting forecast of 415.00 are (round your responses to two decimal places): Year 2005 2006 2007 2008 2009 2010 Forecasted Sales 415.00

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