Forecasting Case Analysis MGT 3332 Spring 2023 ENTERPRISE INDUSTRIES 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: Demand Y = demand for a large size bottle of Fresh (in 100,000) Price the price of Fresh as offered by Enterprise 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 a) Make time series scatter plots of all five variables (five separate graphs). Insert trend line, equation, and R-squared. Observe graphs and provide interpretation of results. b) Construct scatter plots of Demand vs. Diff, Demand vs. Adv, Demand vs. AIP, and Demand vs. Price. Insert fitted linear line, equation, and R-squared. Observe graphs and provide interpretation. Note that Demand is always on the Y axis. c) Obtain the correlation matrix for all six variables and list the variables that have strong correlation with Demand. High correlation is r > 0.50. Explain your findings in plain language. d) Use 3-month and 6-month moving averages to predict the demand for March 2023. 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. e) Use Exponential smoothing forecasts with alpha of 0.1, 0.2, ..., 0.9 to predict March 2023 demand. Identify the value of alpha that results in the lowest MAD. f) Find the monthly seasonal indices for the demand values using Simple Average (SA) method. Find the de- seasonalized demand values by dividing monthly demand by corresponding seasonal indices. g) Use regression to perform trend analysis on the de-seasonalized demand values. Is trend analysis suitable for this data? Find MAD and explain the Excel Regression output (trend equation, r, r-squared, goodness of model). h) Find the seasonally adjusted trend forecasts for March through May 2023. i) Perform simple linear regression analysis with ADV as the independent variable. Write the complete equation, find MAD and explain the Excel Regression output. Make sure to use the de-seasonalized demand data for this model and all future models. j) Repeat part (i) with Diff as the independent variable. k) Construct multiple linear regression model with Period, AIP, Diff, and Adv as independent variables. Formulate the equation, find MAD, and explain the output. Rank variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain. 1) Perform multiple linear regression analysis with Period, Diff, and Adv as independent variables. Formulate the equation and find MAD. Which variable is the most significant predictor of demand? Rank the independent variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain. m) Use the model in part (1) and make forecasts for the following months. Make sure to seasonalize final forecasts. Period Price AIP Adv March 2023 $7.28 $7.41 $10.29 April 2023 $7.17 $7.65 $11.71 May 2023 $7.48 $7.85 $10.32 n) Provide analytical conclusion for the case based on above analysis. Paste M33 BIU V * * fx V V A A B C D E F G H 4 Month Year Period Price AIP Diff Adv Demand Advertising 5 June 2020 1 6.11 5.78 -0.33 5.80 29.43 6 July 2020 2 5.75 6.00 0.25 6.21 31.41 7 August 2020 3 5.71 6.32 0.61 7.41 34.27 8 September 2020 4 5.73 5.71 -0.02 7.95 33.17 9 October 2020 5 5.61 5.85 0.24 7.63 32.96 10 November 2020 6 5.62 5.81 0.19 7.08 31.86 11 December 2020 7 5.61 5.75 0.14 7.35 31.19 12 12 January 2021 8 6.34 5.85 -0.49 7.08 28.33 13 February 2021 9 6.42 5.65 -0.77 6.32 27.01 14 March 2021 10 6.21 6.02 -0.19 5.99 26.38 15 April 2021 11 6.31 6.13 -0.18 7.08 28.11 16 May 2021 12 5.91 6.03 0.12 6.97 30.31 17 June 2021 13 5.73 6.11 0.38 7.63 31.41 18 July 2021 14 5.75 6.22 0.47 7.52 33.61 19 August 2021 15 5.77 6.13 0.36 7.41 36.47 20 September 2021 16 5.82 6.15 0.33 7.41 36.03 21 October 2021 17 5.71 6.18 0.47 7.71 35.35 22 November 2021 18 5.81 6.29 0.48 7.63 34.7 23 December 2021 19 5.72 6.11 0.39 7.41 34.07 24 January 2022 20 5.85 5.76 -0.09 7.08 33.00 25 25 February 2022 21 5.92 5.78 -0.14 7.30 31.19 26 March 2022 22 5.75 5.64 -0.11 8.17 30.96 27 April 2022 23 5.71 5.93 0.22 7.95 32.73 28 May 2022 24 5.55 5.66 0.11 8.17 33.39 29 June 2022 25 5.61 6.12 0.51 8.82 36.24 30 July 2022 26 5.65 6.24 0.59 9.04 38.23 31 August 2022 27 5.72 5.65 -0.07 9.48 41.76 32 September 2022 28 5.75 5.78 0.03 9.59 39.77 33 October 2022 29 5.83 5.85 0.02 9.91 38.01 34 November 2022 30 5.37 6.25 0.88 10.35 37.41 35 December 2022 31 5.42 6.31 0.89 10.57 36.30 36 January 2023 32 5.71 6.43 0.72 10.79 35.59 37 February 2023 33 5.92 6.51 0.59 11.22 35.16 38 March 2023 34 39 April 2023 35 40 May 2023 36 41 42 43 Data Ready + Accessibility: Investigate Merge & K