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 33 sales periods. Each sales period is defined as one month. The variables are as follows: Period = Time period in month 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 1- Download the data from Course Blackboard site into Excel spreadsheet. 2- Make time series scatter plots of all five variables (five graphs). Insert trend line, equation, and R-squared. Observe graphs and provide interpretation. 3- Construct scatter plots of Demand vs. DIFF and Demand vs. ADV, Demand vs. AIP, and Demand vs. Price. Insert fitted line, equation, and R-squared. Observe graphs and provide interpretation. Note that Demand is always on the Y axis. 4- Obtain the correlation matrix for all six variables and rank the variables that have strong correlation with Demand (must use absolute value.) Explain your findings in plain language. 5- Use 3-month and 6-month moving averages to predict the demand for October 2021. Find MAD for both forecasts and identify the preferred one based on each calculation. Is the moving average suitable method for forecasting for this data set? Explain your reasoning. 6- Use Exponential smoothing forecasts with alpha of 0.1, 0.2, ., 0.9 to predict October 2021 demand. Identify the value of alpha that results in the lowest MAD. .... PERIOD PRICE Month/Yr. Jan. 2019 AIP DIFF ADV DEMAND 6.1 5.8 -0.3 5.1 11.1 5.75 5.7 5.7 5.6 2 6 0.25 6.75 13.3 6.3 0.6 7.25 14.5 4 5.7 7.3 14.1 5.85 0.25 7.2 14 5.6 5.8 6.5 13.5 6. 7. 0.2 5.6 5.75 0.15 6.75 13.2 6.3 5.85 -0.45 11.9 6.89 5.8 6.4 5.65 -0.75 11.3 10 6.2 -0.2 5.5 11.8 11 5.9 6.1 0.2 6.5 11.8 12 5.9 0.1 6.25 12.8 Jan. 2020 13 5.7 6.1 0.4 7 13.3 5.75 5.75 14 6.2 0.45 6.9 14.3 15 6.1 0.35 6.8 15.5 16 5.8 6.1 0.3 6.8 15.4 17 5.7 6.2 0.5 7.1 15.1 18 5.8 6.3 0.5 14.8 5.7 5.8 19 6.1 0.4 7.2 14.5 7.5 5.75 5.75 20 -0.05 14 21 5.8 -0.05 7.8 13.8 5.75 5.65 5.9 22 -0.1 8.2 13.8 5.7 5.55 5.6 5.65 23 0.2 8.3 14.3 24 5.65 0.1 8.4 15.4 Jan. 2021 25 6.1 0.5 8.9 15.8 26 6.25 0.6 9.1 16.5 9.3 5.7 5.75 5.8 5.3 5.65 5.75 27 -0.05 17.2 28 9.4 17.6 29 5.85 0.05 9.3 17.3 6.25 6.3 30 0.95 9.4 17.1 31 5.4 0.9 9.5 16.8 32 5.7 6.4 0.7 9.6 16 Sep. 2021 Oct. 2021 33 5.9 6.5 0.6 10 15.5 34 Nov. 2021 35 Dec. 2021 36