Date Rate 4-Jan 2.67 11-Jan 2.71 18-Jan 2.79 25-Jan 2.76 6 1-Feb 2.70 8-Feb 2.63 8 15-Feb 2.66 9 22-Feb 2.65 10 1-Mar 2.76 11 8-Mar 2.62 12 15-Mar 2.59 13 22-Mar 2.44 14 29-Mar 2.41 15 5-Apr 2.50 16 12-Apr 2.56 17 26-Apr 2.51 18 3-May 2.54 19 10-May 2.47 20 17-May 2.39 21 24-May 2.32 22 31-May 2.14 23 7-Jun 2.09 24 14-Jun 2.09 25 21-Jun 2.07 26 28-Jun 2.00 27 5-Jul 2.04 28 12-Jul 2.12 29 19-Jul 2.05 30 26-Jul 2.08 31 2-Aug 1.86 32 9-Aug 1.74 33 16-Aug 1.55 34 23-Aug 1.52 35 30-Aug 1.50 36 6-Sep 1.55 37 13-Sep 1.84 38 20-Sep 1.78 39 27-Sep 1.59 40 4-Oct 1.43 41 11-Oct 1.84 42 18-Oct 1.84 43 25-Oct 1.70 44 1-Nov 1.65 45 8-Nov 1.87 46 15-Nov 1.79 22-Nov 1.72 48 29-Nov 1.68 49 6-Dec 1.79 50 13-Dec 1.80 51 20-Dec 1.92 52 27-Dec 1.88 53(b) Perform exponential smoothing with a = .20. Use both methods A and B to initialize the forecast. Record the statistics of fit. (Round the Mean squared error to 3 decimal places, percent values to 1 decimal place and Forecast to 2 decimal places.) a = .20 Mean squared error 0.030 V Mean absolute percent error 6.9 X % Forecast for period 52 1.84 X (c) Do the smoothing again with a = .10 and then with a = .30, recording the statistics of fit. (Round the Mean squared error to 3 decimal places and percent values to 1 decimal place.) a = .10 a = .30 Mean squared error 0.062 X 0.022 Mean absolute percent error 9.9 X % 5.9 X %