MM-YY	Period	Sales	Forecast
					t	St	Ft
9-Jul	1	3,303
				9-Aug	2	3,360	3,303
9-Sep	3	3,828	3,326
				9-Oct	4	4,257	3,527
9-Nov	5	5,508	3,819
				9-Dec	6	5,205	4,494
10-Jan	7	5,190	4,779
				10-Feb	8	5,058	4,943
10-Mar	9	5,307	4,989
				10-Apr	10	4,563	5,116
10-May	11	4,512	4,895
				10-Jun	12	4,434	4,742
10-Jul	13	4,440	4,619
				10-Aug	14	5,178	4,547
10-Sep	15	5,277	4,800
				10-Oct	16	6,411	4,991
10-Nov	17	7,308	5,559
				10-Dec	18	7,275	6,258
11-Jan	19	7,065	6,665
				11-Feb	20	7,497	6,825
11-Mar	21	7,326	7,094
				11-Apr	22	6,207	7,187
11-May	23	5,976	6,795
				11-Jun	24	5,874	6,467
11-Jul	25	5,970	6,230
				11-Aug	26	6,666	6,126
11-Sep	27	7,575	6,342
				11-Oct	28	8,367	6,835
11-Nov	29	9,051	7,448
				11-Dec	30	9,696	8,089
12-Jan	31	9,594	8,732
				12-Feb	32	9,084	9,077
12-Mar	33	8,955	9,080
				12-Apr	34	8,235	9,030
12-May	35	8,055	8,712
				12-Jun	36	7,767	8,449

Note: The alpha value = 0.4 and initial forecast F2 = S1 = 3,303 units.

Use the existing sales data to determine the appropriate forecasting technique using Excel. The forecasting techniques under consideration are (a) Exponential smoothing; (b) Simple linear regression; and (c) Decomposition using least squares regression. You will have to compute forecasts using each of these techniques and then determine which of the three provides a more accurate forecast based on Mean Absolute Deviation.