The following tabulations are actual sales of units for six months and a starting forecast in January.ACTUAL124FORECAST10412013310986105JanuaryFebruaryMarchAprilMayJunea. Calculate forecasts for the remaining five months using simple exponential smoothing with a = 0.3.Note: Round your answers to the nearest whole number.FebruaryMarchAprilMayJuneSavedForecastsb. Calculate the MAD for all the forecasts, including January's.Note: Round your answer to 1 decimal place.MAD

Simple exponential smoothing is a forecasting method that uses weighted averages of past data to…

Answered: The following tabulations are actual…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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The following data are for calculator sales in units at an electronics store over the past nine weeks: Week Sales Week Sales 1 45 6. 53 2 50 7 59 3 44 59 4 51 9 64 5 57 Use trend projection with regression to forecast sales for weeks 10 - 13. What are the error measures (CFE, MSE, G. MAD, and MAPE) for this forecasting procedure? How about ? Obtain the trend projection with regression forecast for weeks 10 - 13. (Enter your responses rounded to two decimal places.) Period Forecast, F, 10 11 12 13
A large cement manufacturer uses exponential smoothing to forecast demand for a piece of pollution-control equipment. It appears that an increasing trend is present.Month At1 700 2 685 3 648 4 717 5 713 6 728 7 754 8 762 If the initial forecast for month 1 was 650 units and the trend over that period was 0 units, calculate FIT for the 9-month period. Use smoothing constants, Alpha = 0.1 andAlpha = 0.2.
Here are the actual tabulated demands for an item for a nine-month period (January through September). Your supervisor wants to test two moving average forecasting methods to see which one was better over this period. Month Actual Demand Jan 120 Feb 140 March 160 April 180 May 170 June 190 July 150 August 160 September 170 a. Forecast April through September using a three-month moving average and calculate MAD for the forecasts. b. Forecast May through September using a four-month moving average and calculate MAD for the forecasts.
Centerville Bikes and Stuff (CBS) sells motorcycles and accessories. The number of helmets sold by CBS per week for the past six weeks follows. Week 1 2 3 Value 17 12 17 (a) mean absolute error MAE = (b) mean squared error MSE = Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. 4 % 5 6 (d) What is the forecast for week 7? 10 16 15 (c) mean absolute percentage error (Round your answer to two decimal places.) MAPE =
Respond to each of the items using the following time series data. Period Demand 144 2 172 3 157 4 160 144 181 7 160 8 176 9. 149 10 183 11 182 12 149 13 153 14 164 15 153 16 104 Click here for the Excel Data File b. Compute all possible forecasts using a five-period moving average. (Round your answers to 1 decimal place.) Absolute Period Demand 5-period SMA Errors 1 144 172 3 157 4. 160 5 144 181 7 160 8 176 9 149 10 183 11 182 12 149 13 153 14 164 LO
January February March April May June a. Calculate forecasts for the remaining five months using simple exponential smoothing with a = 0.4. February March April May June ACTUAL FORECAST 100 85 96 103 82 66 90 MAD Forecasts 91 93 97 91 81 b. Calculate the MAD for all the forecasts, including January's. (Round your answer to 1 decimal place.)
Centerville Bikes and Stuff (CBS) sells motorcycles and accessories. The number of helmets sold by CBS per week for the past six weeks follows. Week 1 2 Value 17 14 15 3 (b) mean squared error MSE = % 4 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. (a) mean absolute error MAE = 5 6 (d) What is the forecast for week 7? 12 16 15 (c) mean absolute percentage error (Round your answer to two decimal places.) MAPE =
b. Compute 2s control limits for each forecast. (Do not round your intermediate calculations. Round your answers to 2 decimal places.) Forecast Method 1 Method 2 Control Limits
The following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. QUARTER 1 2 5678AWN 3 4 8 SALES 219 237 208 171 169 Quarter 9 10 11 12 192 157 132 Use regression and seasonal indexes to forecast quarterly sales for the next year. Note: Do not round intermediate calculations. Round your answers to the nearest whole number. Forecast
Centerville Bikes and Stuff (CBS) sells motorcycles and accessories. The number of helmets sold by CBS per week for the past six weeks follows. Week 1 2 3 Value 18 13 (a) Construct a time series plot. 20T 18 16- 14- Time Series Value O Week 1 2 3 4 5 6 What type of pattern exists in the data? O The data appear to follow a cyclical pattern. O The data appear to follow a trend pattern. 8 6 O The data appear to follow a seasonal pattern. O The data appear to follow a horizontal pattern. Week 1 2 0 1 3 4 5 6 18 13 15 12 (b) Develop the three-week moving average for this time series. (Round your answers to two decimal places.) Time Series Value 16 14 18 + 2 13 15 4 5 6 15 12 16 3 Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for week 7? 12 16 4 14 Week (c) Use a = 0.2 to compute the exponential smoothing values for the time series. Time Series Value 5 14 Forecast 6 Forecast 7 20 18 16 14 12 WE 10 2 + 7 @o 20 18 0 1 2 3 4 Week 5 6 00 0 1 2 3 4 5 Week 6 7…
Please do not give solution in image formate thanku. Bradley's Copiers sells and repairs photocopy machines. The manager needs weekly forecasts of service calls so that he can schedule service personnel. Use the actual demand in the first period for the forecast for the first week so error measurement begins in the second week. The manager uses exponential smoothing with α = 0.5 Forecast the number of calls for week 6, which is next week. WEEK ACTUAL SERVICE CALLS 1 28 2 32 3 40 4 27 5 30
After using your forecasting model for six months, you decide to test it using a tracking signal. Here are the forecast and actual demands for the six-month period: PERIOD FORECAST ACTUAL May 450 500 June 500 550 July 550 400 August 600 500 September 650 675 October 700 600 Find the tracking signal of each month

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