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The Fresh Detergent Case Enterprise Industries produces Fresh, a brand of liquid detergent. In order to more effectively manage its inventory, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for Fresh over the last 60 sales periods. Each sales period is defined as one month. The variables are as follows: Demand = Y = demand for a large size bottle of Fresh (in 100,000) Price = the price of Fresh as offered by Ent. Industries AIP = the average industry price ADV = Ent. Industries Advertising Expenditure (in $100.000) to Promote Fresh in the sales period. DIFF = AIP - Price = the "price difference" in the sales period 6 Use regression to perform trend analysis on the de-seasonalized demand values. Is trend analysis suitable for this data? Find MAD, the seasonally adjusted trend forecasts for January through March 2021 and explain the Excel Regression output (trend equation, 1. r- squared, goodness of model). Month/Yr. Jan. 2016 PERIOD PRICE 1 5.4 2 5.5 3 6.0 4 6.1 5 5.9 6 5.9 7 5.9 8 6.8 9 6.8 10 6.4 11 6.5 12 6.3 13 6.1 14 6.1 15 6.0 16 6.4 17 6.2 18 6.0 19 6.1 20 6.4 21 6.0 22 6.2 AIP 5.9 6.6 6.4 6.1 6.4 6.3 6.0 6.0 5.8 6.3 6.3 6.2 6.5 6.6 6.3 6.7 6.5 6.8 6.6 6.1 6.1 6.2 DIFF 0.5 1.1 0.4 0.0 0.5 0.4 0.1 -0.8 - 1.0 -0.1 -0.2 -0.1 0.4 0.5 0.3 0.3 0.3 ADV 5.3 6.8 7.3 7.3 7.2 6.5 6.8 5.0 5.8 5.5 6.5 6.3 7.0 7.7 6.8 6.8 7.1 7.0 7.2 7.5 7.8 8.2 DEMAND 13.9 14.5 14.7 14.9 14.9 14.6 14.1 12.0 14.2 13.9 13.9 13.8 14.0 14.5 16.0 15.7 15.8 15.2 15.9 16.2 15.0 16.9 Jan. 2017 0.8 0.5 -0.3 0.1 0.0 Jan. 2018 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 6.1 6.0 6.1 5.9 6.0 6.3 6.0 5.7 5.6 6.2 6.4 6.5 6.2 6.7 6.9 6.9 6.7 7.0 7.1 7.2 7.2 6.0 6.2 6.7 6.9 5.8 5.8 6.0 6.7 6.4 7.0 7.2 -0.1 0.2 0.6 1.0 -0.2 -0.5 0.0 1.0 0.8 0.8 0.8 -0.6 -0.2 -0.5 -0.9 -0.6 -0.2 - 1.0 -1.0 -0.9 -0.8 -0.8 -1.2 8.3 8.4 8.9 9.1 9.3 9.4 9.3 9.4 9.5 9.6 9.7 9.9 9.8 17.1 16.9 17.4 17.7 17.6 18.4 18.6 17.4 18.4 17.6 16.7 18.2 18.5 19.1 19.0 19.0 19.8 19.8 20.0 20.9 19.6 19.5 18.4 6.0 6.2 9.9 Jan. 2019 10.1 10.2 10.5 6.0 6.3 6.5 6.0 6.1 6.3 6.4 6.5 6.0 10.3 9.9 10.5 10.6 7.3 10.5 11.6 7.2 -0.9 10.1 19.3 -1.0 10.3 6.2 5.9 6.0 6.4 10.7 Jan. 2020 10.9 7.1 6.9 7.2 7.3 7.4 7.5 7.0 6.8 6.5 10.8 11.1 6.5 6.2 6.8 11.2 11.6 7.4 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 19.3 19.9 20.0 20.1 20.1 20.2 21.1 20.6 20.7 21.3 21.4 21.5 21.8 21.5 6.9 -1.2 -0.9 -0.9 -1.0 -0.8 0.0 -0.5 -0.8 -0.4 -0.2 -0.7 -0.7 -1.0 11.5 11.6 11.9 11.8 7.3 7.3 7.2 7.5 7.5 7.5 6.5 6.9 7.0 6.8 6.8 6.5 11.9 12.0 11.9 Jan. 2021 Feb. 2021 Mar. 2021