Prepare a graph of the monthly forecasts and average forecast demand for Chicago Paint Corp., a manufacturer of specialized paint for artists.Compute the demand per day for each month (round your responses to one decimal place).MonthJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptember222 22 2 2 2 2Production Days23Demand ForecastDemand per Day950191,050211,250201,350231,200201,300221,250211,150211,150October211,000November211,000December19950 Choose the correct graph of the forecast demands below.OA.6560-55-50-45-○ C.7065160-55-50-45Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.L40-Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.Q( B65-60-55-50-45-○ D.Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.70165-60-55-50-Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec.

The answer is aDemand per day=demand forecast/month Month Demand. Demand…

Answered: Prepare a graph of the monthly…

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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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 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 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?
Given the following history, use a three-quarter moving average to forecast the demandfor the third quarter of this year. Note that the 1st quarter is Jan, Feb, and Mar; 2ndquarter Apr, May, Jun; 3rd quarter Jul, Aug, Sep; and 4th quarter Oct, Nov, JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DECLast year 120 140 145 195 205 220 165 155 145 220 245 265This year 140 145 200 210 240 255
My App is a small but growing start-up that sees demand for several of its apps increasing quickly. The table below shows the last six months of downloads. Use a forecast for the first month of 220,000, an initial trend forecast of 35,000, and smoothing parameters of 015 for both demand smoothing and trend smoothing Month (t) Monthly Application Downloads Forecast for Next Month Trend 220,000.00 35,000.00 200,000 2 250, 100 334,000 380,000 440,000 500,000 (Round your answers to 2 decimal places.) Complete the table above, filling in the "Forecast for next month" and "Trend" columns, using double a. exponential smoothing (Round your answers to 2 decimal places.) Month 7's forecast is b.
st Info Period 1 2 3 4 5 6 7 8 9 10 K Develop two exponential smoothing forecasts for periods 2 through 11. For the first forecast, use a = 0.3. For the second, use α = 0.7. Assume that your forecast for period 1 was 205. Click the icon to view the time series data. Find the exponential smoothing forecast for each period when a = 0.3 (enter your responses rounded to one decimal place). Demand 222 249 222 228 235 155 161 153 163 152 X Period 1 2 Demand 222 249 Forecast (α = 0.3) 205
A manager uses a trend equation plus quarterly relatives to predict demand. Quarter relatives areSR1 = .90, SR2 = .95, SR3 = 1.05, and SR4 = 1.10. The trend equation is: Ft= 10 + 5t. Over thepast nine quarters, demand has been as follows:Period, t 1 2 3 4 5 6 7 8 9Demand 14 20 24 31 31 37 43 48 52Is the forecast performing adequately? Explain.
Given the following history, use a three-quarter moving average to forecast the demand for the third quarter of this year. Note that the 1st quarter is Jan, Feb, and Mar; 2nd quarter Apr, May, Jun; 3rd quarter Jul, Aug, Sep; and 4th quarter Oct, Nov, Dec. (Round final answer to a whole number.) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Last year 165 185 200 230 240 265 210 200 195 265 290 315 This year 175 200 165 260 260 200 Forecast for the third quarter
The following data show the number of liters of gasoline sold by Mackrin's in Colorado for the past 12 weeks. Which forecasting model would you recommend to Mackrin's manager? Why? What demand forecast would you recommend for week 13? Week Actual Demand (1000 liters) Forecasting (1000 liters) FSMA /A-F/ FWMA /A-F/ FES, α=0.3 /A-F/ 1 17 2 21 17 4 3 19 18.2 0.8 4 23 19 4 18.44 4.56 5 18 21 3 20.8 2.8 19.81 1.81 6 16 20 4 20 4 19.3 3.3 7 20 19 1 18.3 2.3 18.31 1.69 8 18 18 0 18.7 0.7 18.82 0.82 9 22 18 4 18.2 4.2 18.6 3.4 10 20 20 0 19.8 0.2 19.62 0.38 11 15 20 5 20.2 5.2 19.73 4.73 12 22 19 3 18.2 3.8 18.3 3.7 13 19 19.5 19.41 MAD 2.67 2.9 2.7 MSE 10.22…
Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period simple moving average method to forecast the number of sales to expect next month. Month Demand Forecast 1 308 341 388 343.3 3 344 364.7 4 400 346.7 341 377.3

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