Many forecasting models use parameters that are estimated using nonlinear optimization. The basic exponential smoothing model for forecasting sales is F+ -a + (1 - 0) where F+ - forecast of sales for period + 1 Y actual value of sales for period Ft - forecast of sales for periodt -smoothing constant 0 sas1 This model is used recursively; the forecast for time period t + 1 is based on the forecast for period of the observed value of sales in period, Ye and the smoothing parameter a. The use of this model to forecast sales for 12 months is illustrated in the table below with the smoothing constanta - 0.3. The forecast errors, YF, are calculated in the fourth column. The value of an is often chosen by minimizing the sum of squared forecast errors, commonly referred to as the mean squared error (MSE). The last column of Table shows the square of the forecast error and the sum of squared forecast errors. Week (t) EXPONENTAL SMOOTHING MODEL FOR 0-0,3 Observed Forecast Forecast Error Value Squared Forecast Error (0) (Y) (Y- 19 19.00 0.00 0.00 18 19.00 -1.00 1.00 21 18.70 2.30 5.29 21 19.39 1.61 2.59 1 2 3 4 5 19 19.87 -0.87 0.76 5.81 6 12 19.61 -2.61 18.83 20 19 1.17 -0.18 1.37 0.03 19.18 19.13 9 24 4.87 23.72 10 23 20.59 2.41 7.31 11 14 21.31 5.81 53.44 34.57 12 25 19.12 5.88 SUM 135.39 in using exponential smoothing models, we try to choose the value of a that provides the best forecasts. Build an Excel Solver or LINGO optimization model that will find the smoothing parameter, o, that minimizes the sum of soured forecast errors. You may find it easiest to put table into an Excel spreadsheet and then use Solver to find the optimal value of a. If required, round your answer for a to three decimal places and the answer for the resulting sum of squared errors to two decimal places. The optimal value of als and the resulting sumn of squared errors is