Gere D 0 3 Month 1 6 Sales 110 Parts H 4 5 125 105 SITINE 04 125 T : OPE 10 11 13 95 14 Parth 02 aatt The Series 110 For for Wh 06 501 WA WA 4 V 05 21 W 065 ANCA 50 Data w Home Insert Draw Page Layout Formulas Data Review View Help Editing Arial 10 B dev *** General A C G H L D Smoothing constant (Alpha) 0.2 Forecast Error Formula Month 1 2 3 4 5 NA Time-Series Value 110 130 125 105 90 125 150 140 105 85 95 105 ANA NA ONIA NA NA WNIA UNA MNIA NA ANA NA ANA ONA ENJA NA NA NA WWA NNIA WNA UNA NA 10 11 12 13 Parts Smoothing constant (Alpha) 0.5 Month Forecast Error Formula 2 3 Time-Series Value 110 130 125 100 ONA WNA GNIA NA NA 13 NINA ANA Parte Smoothing constant (Alpha) 0.5 Forecast Error Formula Month 1 2 3 4 5 6 7 8 9 10 11 12 13 Time-Series Value 110 130 125 105 90 125 150 140 105 85 35 105 WNA ONA ANA ANA ANA NA N/A UNA NA MNA WNA MNJA MNA WNIA WNIA WNIA NIA ANA NA NA WNIA WNIA ONIA Alpha 0.2 MSE (to 2 decimals) NA 0.5 INA a. Choose the correct time series plot for the data. Sales 150-1 100 A. 50- Month 10 12 Sales 150+ 100 B. 50 Month 2 3 10 12 Sales 150+ m 100- C. 50- Month 2 10 12 Sales 150+ 100 D. 50 Month 16 12 The correct time series plot is What type of pattern exists in the deta? b. Use a = 0.2 to compute the exponential smoothing forecasts for the time series (to 2 decimals). Month Forecast 1 Time-Series Value 110 130 2 3 125 4 105 5 90 6 125 7 150 8 140 9 105 10 85 11 95 12 105 13 c. Use a smoothing constant of a = 0.5 to compute the exponential smoothing forecasts (to 2 decimals). Time-Series C. Use a smoothing constant of a = 0.5 to compute the exponential smoothing forecasts (to 2 decimals). Time-Series Month Value Forecast 1 110 2 130 NH 3 125 4 105 5 90 6 125 7 150 8 140 9 105 10 85 11 95 12. 105 13 Compute MSE (to 2 decimals). MSE (a=0.2): MSE (a=0.5): Does a smoothing constant of 0.2 or 0.5 appear to provide more accurate forecasts based on MSE? provides more accurate forecasts based on MSE