Consider the following time series: Quarter Year 1 Year 2 Year 3 1 71 68 62 2 49 41 51 3 58 60 53 4 78 81 72 (a) Choose a time series plot. 100 80 60 yt 40 20 0 9 10 11 12 Periodt 100 80 60 yt 40 20 0 10 11 12 Period (t) (IND) 100 80 60 yt 40 20 + 0 + + 3 N + GO 5 10 11 12 (iv) 100 80 60 yt 40 20 1 2 3 4 5 6 7 8 9 10 11 12 Period(t) - Select your answer What type of pattern exists in the data? Is there an indication of a seasonal pattern? - Select your answer - (b) Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2,0 otherwise; Qtr3 = 1 if quarter 3,0 otherwise. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) Qtr1 Qtr3 (c) Compute the quarterly forecasts for next year. Year Quarter Ft Qtrz + 4 1 4 2. 2 4 4 4 Consider the following time series: Quarter Year 1 Year 2 Year 3 1 71 68 62 2 49 41 51 3 58 60 53 4 78 81 72 (a) Choose a time series plot. 100 80 60 yt 40 20 0 9 10 11 12 Periodt 100 80 60 yt 40 20 0 10 11 12 Period (t) (IND) 100 80 60 yt 40 20 + 0 + + 3 N + GO 5 10 11 12 (iv) 100 80 60 yt 40 20 1 2 3 4 5 6 7 8 9 10 11 12 Period(t) - Select your answer What type of pattern exists in the data? Is there an indication of a seasonal pattern? - Select your answer - (b) Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2,0 otherwise; Qtr3 = 1 if quarter 3,0 otherwise. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) Qtr1 Qtr3 (c) Compute the quarterly forecasts for next year. Year Quarter Ft Qtrz + 4 1 4 2. 2 4 4 4