I need help in this question. Please do it correctly and dont make it wrong. Please do it on MS WORD and copy/paste here and use the below formulas to do it.

Question 5 (40 points) The last seven weeks of demand at a new car dealer are shown below. Week Sales 1 36 2 60 3 48 4 70 5 60 6 64 7 72 UA a) Use a three-period weighted moving average to determine a forecast for the week 4, week 5, week o, and week 7 using weights of 1, 3, and 6 as shown in the table given below. Calculate the MAD. (20 points) Weights Applied 6 3 1 Time Period Last week Two weeks ago Three weeks ago Week Sales Forecasting 1 36 2 60 3 48 4 70 5 60 6 64 7 72 Activate Wind b) Use exponential smoothing with an alpha of 0.6 to determine a forecast from week 2 to week 7 with an initial forecast of 18 for Week 1. Calculate the MAD. (15 points) Week Sales Forecasting 1 36 36 2 60 3 48 4 70 5 60 6 64 7 72 c) Based on these calculations, which method seems more accurate? Show your work to support your results (5 points) Weighted Moving Average The most recent values in a time series are given more weight in computing a forecast The choice of weights, w, is somewhat arbitrary and involves some trial and error F, = w;(4)+w;-(4-1)+...+w,-n(4,-n) where w, = weight for period t, w: -1 = weight for period t-1, etc. 4. = the actual value for period t, 4-1 = the actual value for period t-1, etc. (weight for period t) Weighted x (Actual value in period t) moving average weights 3-22 Styles Shape Ettects "E Convert to SmartArt Paragraph Font Drawing E Weighted Moving Average (cont.) Period Weights Applied 3 Last month Two months ago Three months ago Sum of weights 6 Actual Shed Sales 3-Month Weighted Moving Average Month January February March April May June July 10 12 13 16 19 23 26 [(3 x 13) + (2 x 12) + (10)]/6 = 12'16 [(3 x 16) + (2 x 13) + (12)]/6 = 141/3 [(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 = 20112 3-23 LO 3.9 Exponential Smoothing A weighted averaging method that is based on the previous forecast plus a percentage of the forecast error F, = F,- +a(4.-1 F,-1) where F, = Forecast for period t F- = Forecast for the previous period a=Smoothing constant 4_1 = Actual demand or sales from the previous period Exponential Smoothing (cont.) Suppose we use weight a = 0.2: Time Period (t) Sales (At-1) Forecast from prior period (Ft-1) Exponentially Smoothed Value for this period (Ft) F1 = A, since no prior information exists. 1 2 3 4 5 6 7 8 etc. 23 40 25 27 32 48 33 37 etc 23.000 26.400 26.120 26.296 27.437 31.549 31.840 etc. 23.000 23+(-2)(40-23)=26.400 26.4+(-2)(25-26.4)=26. 120 26.12+(-2)(27-26.12)=26.296 26.296+-2)(32-26.296)=27.437 27.437+(-2)(48-27.437)=31.549 31.549+(-2)(33-31.549)=31.840 31.840+(-2)(37-31.840)=32.872 etc. LO 210 3-25