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B H Month 1 Parta Use the area below to draw a time series plot. NONOOM NO CONCO O Sales 110 130 400 120 110 95 125 140 140 105 80 100 110 10 EQ 22 Part b constant (Alpha) 0.2 Forecast Error Formula 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Month 1 2 3 4 5 6 7 8 9 10 11 12 13 Time-Series Value 110 130 120 110 95 125 140 140 105 80 100 110 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 39 40 Part Smoothing constant (Alpha) 0.5 Forecast Error Formula 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 Month 1 2 3 4 5 6 7 8 9 10 11 12 13 Time-Series Value 110 130 120 110 95 125 140 140 105 80 100 110 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A MSE (to 2 decimals) Alpha 0.2 #N/A 58 59 60 61 62 63 0.5 #N/A The time series showing the sales of a particular product over the past 12 months is contained in the Excel Online file below. Construct a spreadsheet to answer the following questions. X Open spreadsheet a. Choose the correct time series plot for the data. Sales 150+ 100- m A. 50- Month 6 10 12 Sales 150+ 100+ B. 50 Month 10 12 Sales 150+ 100- C. 50 Month 8 10 12 Sales 150+ 100- D. 50 Month -00 10 12 The correct time series plot is What type of pattern exists in the data? b. Use a = 0.2 to compute the exponential smoothing forecasts for the time series (to 2 decimals). Month Forecast 1 2 Time-Series Value 110 130 3 120 4 110 5 95 6 125 7 140 TTTTTT 8 140 9 105 10 80 11 100 12 110 13 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 2 3 120 4 110 95 6 125 7 140 TITUTI 8 140 9 105 10 80 11 100 12 110 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. B H Month 1 Parta Use the area below to draw a time series plot. NONOOM NO CONCO O Sales 110 130 400 120 110 95 125 140 140 105 80 100 110 10 EQ 22 Part b constant (Alpha) 0.2 Forecast Error Formula 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Month 1 2 3 4 5 6 7 8 9 10 11 12 13 Time-Series Value 110 130 120 110 95 125 140 140 105 80 100 110 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 39 40 Part Smoothing constant (Alpha) 0.5 Forecast Error Formula 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 Month 1 2 3 4 5 6 7 8 9 10 11 12 13 Time-Series Value 110 130 120 110 95 125 140 140 105 80 100 110 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A MSE (to 2 decimals) Alpha 0.2 #N/A 58 59 60 61 62 63 0.5 #N/A The time series showing the sales of a particular product over the past 12 months is contained in the Excel Online file below. Construct a spreadsheet to answer the following questions. X Open spreadsheet a. Choose the correct time series plot for the data. Sales 150+ 100- m A. 50- Month 6 10 12 Sales 150+ 100+ B. 50 Month 10 12 Sales 150+ 100- C. 50 Month 8 10 12 Sales 150+ 100- D. 50 Month -00 10 12 The correct time series plot is What type of pattern exists in the data? b. Use a = 0.2 to compute the exponential smoothing forecasts for the time series (to 2 decimals). Month Forecast 1 2 Time-Series Value 110 130 3 120 4 110 5 95 6 125 7 140 TTTTTT 8 140 9 105 10 80 11 100 12 110 13 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 2 3 120 4 110 95 6 125 7 140 TITUTI 8 140 9 105 10 80 11 100 12 110 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