Sales of Volkwagen’s popular Beetle have grown steadily at autodealership in Nevada during the past 5 years (see table below).The sales manager had predicted before the new model wasintroduced that first-year sales would be 410 VWs. Usingexponential smoothing with a weight of α= .30, develop forecastfor years 2 through 6.Year Sales Forecast1 450 4102 4953 5184 5635 5846 ?2. The following data come from regression line projections:Period Forecast Values Actual values1 410 4062 419 4233 428 4234 435 440Compute the MAD and MSE. Solve the following:1. Sales of Volkwagen's popular Beetle have grown steadily at autodealership in Nevada during the past 5 years (see table below).The sales manager had predicted before the new model wasintroduced that first-year sales would be 410 VWs. Usingexponential smoothing with a weight of a= .30, develop forecastfor years 2 through 6.YearSalesForecast14504102495351845635584?2. The following data come from regression line projections:Actual valuesPeriod1Forecast Values41040624194234284234435440Compute the MAD and MSE.

Forecasting is the process of predicting the future demand based on the previous data and demand.

Solve the following: 1. Sales of Volkwagen's popular Beetle have grown steadily at auto dealership in Nevada during the past 5 years (see table below). The sales manager had predicted before the new model was introduced that first-year sales would be 410 VWs. Using exponential smoothing with a weight of a= .30, develop forecast for years 2 through 6. Year 1 Sales Forecast 450 410 495 518 563 584 5 6 2. The following data come from regression line projections: Actual values 406 Period 1 Forecast Values 410 2 419 423 428 423 4 435 440 Compute the MAD and MSE. 23

Solve the following: 1. Sales of Volkwagen's popular Beetle have grown steadily at auto dealership in Nevada during the past 5 years (see table below). The sales manager had predicted before the new model was introduced that first-year sales would be 410 VWs. Using exponential smoothing with a weight of a= .30, develop forecast for years 2 through 6. Year 1 Sales Forecast 450 410 495 518 563 584 5 6 2. The following data come from regression line projections: Actual values 406 Period 1 Forecast Values 410 2 419 423 428 423 4 435 440 Compute the MAD and MSE. 23

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 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?
Solve the following:1. Sales of Volkwagen’s popular Beetle have grown steadily at autodealership in Nevada during the past 5 years (see table below). The sales manager had predicted before the new model was introduced that first-year sales would be 410 VWs. Using exponential smoothing with a weight of α= .30, develop forecast for years 2 through 6.Year Sales1 450 4102 4953 5184 5635 5846?2. The following data come from regression line projections:ForecastPeriod 1234Forecast Values 410419428435Actual values 406423423440Compute the MAD and MSE.
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?
Practice Problem: Usins the data on a hospital’s revenue: (1) Use simple exponential smoothing to make predictions for the hospital’s revenues during the next four quarters with α = 0.30. (2) Use Trend-Adjusted Exponential Smoothing(i.e. Holt’s method) to make forecasts for the hospital’s revenues during the next four quarters. Assume α = 0.05, β = 0.65, initial revenue forecast = 1209285.75 and initial trend forecast = 15714. (3) Using least-squares regression with seasonal index decomposing method, forecast the hospital’s revenues during the next four quarters.
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1. Forecasting stationary series The company Handy Inc. produces a solar-powered electronic calculator that has experienced the following monthly sales history for the first four months of the year (in thousands of units): January February. March. April sales 23.3. 72.3. 30,3. 15.5 a) Find the forecast for May using the Moving Averages method with N = 3. b) Assume that the forecast for January was 25. Determine the one-step ahead forecast for February through May, using exponential smoothing with a smoothing constant of α = 0.15. c) Repeatthecalculationinproblembforavalueofα=0.40. d) Compare the MAD (Mean Absolute Deviation) and MSE (Mean Squared Error) for the forecast for February to April in problems b and c. Comment the accuracy of the forecasts. e) The demand in May turned out to be 30. Which of the three forecasting method were most accurate for this prediction? 2. Regression analysis 3 000 000 2 500 000 2 000…
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4. A local moving company has collected data on the number of moves they have been asked to perform over the past two years. Moving is highly seasonal, so the owner/operator, who is both burly and highly educated, decides to apply the multiplicative seasonal method to forecast the number of customers for the coming year. The equation for the trend line of yearly sales is F1 = 100 + 60t. Please forecast demand for each quarter in Year 3. (Round the forecasts to whole numbers and show all calculations t Complete the table below and forecast the sales of Year 3 by quarter. Copy the table below, paste to the answer box and fill in your answers. You need to take a picture of your work and upload the picture in next question. Year 1 Year 2 Year 3 Quarter Demand Quarter Demand Quarter Demand 1 28 1 45 1 43 60 2 120 140 4 49 55 4 Total 240 Total 300 Total Average Average Average For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac).
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 =…