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Based on the following equation for a moving average forecast, what would have been the three week moving average forecast for week 5 for Small Town Restaurant (see downloaded file for actual demand)? Provide two decimal places and use normal rounding. ΣDemand in Previous n Periods MA

Based on the following equation for a moving average forecast, what would have been the three week moving average forecast for week 5 for Small Town Restaurant (see downloaded file for actual demand)? Provide two decimal places and use normal rounding. ΣDemand in Previous n Periods MA

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Solve for Week 5 as well as Week 4 pls with given data

¶ ¶ Situation: The "Small Town Restaurant" uses a forecast of weekly sales revenue to plan their inventory ordering and staffing for the upcoming week. They have been using a regression forecast for the past year and wonder if it is the most accurate method. You have been brought in to determine if another forecasting technique would be more accurate. Data for their weekly sales for the past year is below: ¶ Week Revenue. X Week 1 2 $3,016 3 $3,062.¤ ¤ 4 $ 3,074. X 5 $3,002. 6 ----3,054-¤¤ 7 $3,002.¤ ¤ 8 9 $3,078¤ ¤ 10 $3,010- X 11 $3,148. 12 $3,146 $3,198. 13 X ¶ $3,157.** $ Forecasting S $ 2,984-¤¤ A - Small Town Restaurant Revenue. 14 $3,157 ¤ $3,016 ¤ 15 16 $3,019 ¤ 17 $3,045 ¤ 18 19 $3,046 ¤ $3,157 ¤ $3,016 ¤ $3,078 ¤ 22 $2,984 20 21 ¤ 23 $3,002 ¤ 24 $3,054 ¤ 25 $3,002 ¤ 26 $2,941 X A Week Revenue. 27 $2,899 ¤ 28 $2,942 ¤ 29 $3,062 X ¤ 30 $3,074 31 $3,013 ¤ ¤ 32 $3,067 33 $3,148 ¤ 34 $3,146 ¤ 35 $3,198 ¤ 36 $3,082 ¤ 37 $3,146 ¤ 38 $3,150 ¤ 39 $3,221 ¤ X B Week Revenue. ¤ 40 $3,298. 41 $3,201. 42 $3,156. 43 $3,017. X 44 $3,156. 45 $3,097. 46 $ 2,978. X 47 $3,120 48 $3,156. 49 $3,048. 50 $2,948. 51 $ 2,840. 52 $2,921. X $ 159,532 Annual total You will use this data in the next few steps to complete various time series forecasts and compare them to determine the most accurate method. ¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤ X{ X{ X{ X{
Transcribed Image Text:¶ ¶ Situation: The "Small Town Restaurant" uses a forecast of weekly sales revenue to plan their inventory ordering and staffing for the upcoming week. They have been using a regression forecast for the past year and wonder if it is the most accurate method. You have been brought in to determine if another forecasting technique would be more accurate. Data for their weekly sales for the past year is below: ¶ Week Revenue. X Week 1 2 $3,016 3 $3,062.¤ ¤ 4 $ 3,074. X 5 $3,002. 6 ----3,054-¤¤ 7 $3,002.¤ ¤ 8 9 $3,078¤ ¤ 10 $3,010- X 11 $3,148. 12 $3,146 $3,198. 13 X ¶ $3,157.** $ Forecasting S $ 2,984-¤¤ A - Small Town Restaurant Revenue. 14 $3,157 ¤ $3,016 ¤ 15 16 $3,019 ¤ 17 $3,045 ¤ 18 19 $3,046 ¤ $3,157 ¤ $3,016 ¤ $3,078 ¤ 22 $2,984 20 21 ¤ 23 $3,002 ¤ 24 $3,054 ¤ 25 $3,002 ¤ 26 $2,941 X A Week Revenue. 27 $2,899 ¤ 28 $2,942 ¤ 29 $3,062 X ¤ 30 $3,074 31 $3,013 ¤ ¤ 32 $3,067 33 $3,148 ¤ 34 $3,146 ¤ 35 $3,198 ¤ 36 $3,082 ¤ 37 $3,146 ¤ 38 $3,150 ¤ 39 $3,221 ¤ X B Week Revenue. ¤ 40 $3,298. 41 $3,201. 42 $3,156. 43 $3,017. X 44 $3,156. 45 $3,097. 46 $ 2,978. X 47 $3,120 48 $3,156. 49 $3,048. 50 $2,948. 51 $ 2,840. 52 $2,921. X $ 159,532 Annual total You will use this data in the next few steps to complete various time series forecasts and compare them to determine the most accurate method. ¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤ X{ X{ X{ X{
Based on the following equation for a moving average forecast, what would have been the three week moving average forecast for week 5 for Small Town Restaurant (see downloaded file for actual demand)? Provide two decimal places and use normal rounding. ΜΑ = Demand in Previous n Periods n
Transcribed Image Text:Based on the following equation for a moving average forecast, what would have been the three week moving average forecast for week 5 for Small Town Restaurant (see downloaded file for actual demand)? Provide two decimal places and use normal rounding. ΜΑ = Demand in Previous n Periods n
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