a) Using a 3-year moving average, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses to onedecimal place):YearForecast4YearForecast5465768b) Using a 3-year weighted moving average in which the registrations in the most recent year are given a weight of 2, and registrations in theother 2 years are each given a weight of 1, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses toone decimal place):7981091110121112 Data collected on the yearly registrations for a Six Sigma seminar at the Quality College are shown in the following table:Year14.0YearForecast26.043 44.0 5.05Registrations (000)This exercise contains only parts a and b.a) Using a 3-year moving average, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses to onedecimal place):6105 6 7 8 910.0 8.0 7.0 9.0 12.0 14.078910111115.012Db) Using a 3-year weighted moving average in which the registrations in the most recent year are given a weight of 2, and registrations in thenther 2 veare are each niven a weight of 1 the forecasted registratione (in thousande) for voare 4 through 12 are fround your roenoncos fo

Forecasting is a technique used to predict future outcomes on the basis of past data. In businesses…

Answered: a) Using a 3-year moving average, the…

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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using minitab
The following data are for calculator sales in units at an electronics store over the past nine weeks: Week Sales Week Sales 1 45 53 2 50 7 59 3 44 8. 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, o, 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 64.81 11 67.06 12 69.31 13 71.56 Obtain the error measures. (Enter your responses rounded to two decimal places.) CFE MSE MAD МАРЕ
A forecast for the first six months of the year revealed a tendency to underpredict the actual demand for the revitalized Hubig’s Pies plant in the Marigny. Month Actual Forecast January 675 600 February 720 700 March 640 620 April 510 495 May 480 410 June 565 535 What is the mean squared error of this forecast? a. 1,694 b. 1,873 c. 2,075 d. 1,469
The following tabulations are actual sales of units for six months and a starting forecast in January. ACTUAL 124 FORECAST 104 120 133 109 86 105 January February March April May June a. Calculate forecasts for the remaining five months using simple exponential smoothing with a = 0.3. Note: Round your answers to the nearest whole number. February March April May June Saved Forecasts b. Calculate the MAD for all the forecasts, including January's. Note: Round your answer to 1 decimal place. MAD
Here are the data for the past 21 months for actual sales of a particular product: LAST YEAR THIS YEAR January 315 February 418 March 390 April 430 May 400 [ June 480 July 430 August 320 September 385 October 530 November 592 555 December 300 400 350 460 390 395 355 245 340 Develop a forecast for the fourth quarter using a three-quarter, weighted moving average. Weight the most recent quarter 0.50, the second most recent 0.25, and the third 0.25. Do the problem using quarters, as opposed to forecasting separate months. (Round your answer to 2 decimal places.) Forecast for the fourth quarter
Freya is a new name in the retail industry that just started off its journey. With the right strategies, the brand’s grandeur will only rise with time. However, the founders really want to predict the customer demand right for the month of June. Hence, they need to figure out the right forecasting techniques. They are considering 2-period Moving Average, Naive technique, and Exponential Smoothing (use any value between .10 - .18 as the value of alpha). Figure out the better forecasting technique among 2 periods Moving Average, Naive technique, and Exponential Smoothing by doing the error calculation. Period Demand 2period Moving Average Naive technique Exponential Smoothing January 80 85 82 70 February 84 82 ** ** March 89 ** ** ** April 95 ** ** ** May 98 ** ** **
2
14 Community General Hospital finds itself treating many bicycle accident victims. Data from the last seven 24-hour periods is shown below: Day Bicycle Victims 1 8 2 14 3 8 4 14 5 18 6 15 With an alpha value of 0.31 and a starting forecast in day 3 equal to the 21 , what is the exponentially smoothed forecast for day 4? (Round to two decimal places) 15 What is the exponentially smoothed forecast for day 5? (Round to two decimal places)
Consider the following yearly demand values for a particular product. Year Demand 1 360 389 3 410 4 381 368 374 (a) Produce forecast for year 4,5,6, and 7 by using 3-week moving average. (b) Produce forecast for year 4,5,6, and 7 by using 3-week weighted moving average where weights are as follows; wt–3 = .1, wt-2 = .3, and wt-1 = .6. (c) Produce forecast for year 4,5,6, and 7 by using exponential smoothing with a = .2. Please assume that the forecast for the first year is 360.
Apply the moving average with base m=4 forecasting technique of the data to estimate the demand in week 13.
Given is a historical time series for job services demand in the prior 6 months. Month 1 2 3 4 5 6 Demand 799 816 789 814 815 805 The Period 6 forecast by using weighted moving average with weights of 0.05, 0.25, 0.40, and 0.30 = Blank 1 (in 2 decimal places)
With the multiplicative seasonal method of forecasting: the seasonal amplitude is a constant, regardless of the magnitude of average demand. there can be only four seasons in the time-series data. the times series cannot exhibit a trend. seasonal factors are multiplied by an estimate of average demand to arrive at a seasonal forecast.

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