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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: 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 seasonal indices. \begin{tabular}{|c|c|c|c|c|c|c|} \hline Month/Yr. & PERIOD & PRICE & AIP & DIFF & ADV & DEMAND \\ \hline Jan.2018 & 1 & 5.4 & 5.9 & 0.5 & 5.3 & 13.9 \\ \hline & 2 & 5.5 & 6.6 & 1.1 & 6.8 & 14.5 \\ \hline & 3 & 6.0 & 6.4 & 0.4 & 7.3 & 14.7 \\ \hline & 4 & 6.1 & 6.1 & 0.0 & 7.3 & 14.9 \\ \hline & 5 & 5.9 & 6.4 & 0.5 & 7.2 & 14.9 \\ \hline & 6 & 5.9 & 6.3 & 0.4 & 6.5 & 14.6 \\ \hline & 7 & 5.9 & 6.0 & 0.1 & 6.8 & 14.1 \\ \hline & 8 & 6.8 & 6.0 & .0 .8 & 5.0 & 12.0 \\ \hline & 9 & 6.8 & 5.8 & -1.0 & 5.8 & 14.2 \\ \hline & 10 & 6.4 & 6.3 & -0.1 & 5.5 & 13.9 \\ \hline & 11 & 6.5 & 6.3 & -0.2 & 6.5 & 13.9 \\ \hline & 12 & 6.3 & 6.2 & -0.1 & 6.3 & 13.8 \\ \hline & 13 & 6.1 & 6.5 & 0.4 & 7.0 & 14.0 \\ \hline Jan.2019 & 14 & 6.1 & 6.6 & 0.5 & 7.7 & 14.5 \\ \hline & 15 & 6.0 & 6.3 & 0.3 & 6.8 & 16.0 \\ \hline & 16 & 6.4 & 6.7 & 0.3 & 6.8 & 15.7 \\ \hline & 17 & 6.2 & 6.5 & 0.3 & 7.1 & 15.8 \\ \hline & 18 & 6.0 & 6.8 & 0.8 & 7.0 & 15.2 \\ \hline & 19 & 6.1 & 6.6 & 0.5 & 7.2 & 15.9 \\ \hline & 20 & 6.4 & 6.1 & -0.3 & 7.5 & 16.2 \\ \hline & 21 & 6.0 & 6.1 & 0.1 & 7.8 & 15.0 \\ \hline & 22 & 6.2 & 6.2 & 0.0 & 8.2 & 16.9 \\ \hline & 23 & 6.1 & 6.0 & .0 .1 & 8.3 & 17.1 \\ \hline & 24 & 6.0 & 6.2 & 0.2 & 8.4 & 16.9 \\ \hline \end{tabular} Dataset Sheet? Sheets Dataset Sheet2 Sheet3