- Consider The Following Time Series Quarter Year 1 Year 2 Year 3 1 2 3 4 74 44 61 78 100 80 60 40 71 20 36 0 63 A Choo 1 (19.21 KiB) Viewed 22 times
- Consider The Following Time Series Quarter Year 1 Year 2 Year 3 1 2 3 4 74 44 61 78 100 80 60 40 71 20 36 0 63 A Choo 2 (17.95 KiB) Viewed 22 times
- Consider The Following Time Series Quarter Year 1 Year 2 Year 3 1 2 3 4 74 44 61 78 100 80 60 40 71 20 36 0 63 A Choo 3 (26.68 KiB) Viewed 22 times
- Consider The Following Time Series Quarter Year 1 Year 2 Year 3 1 2 3 4 74 44 61 78 100 80 60 40 71 20 36 0 63 A Choo 4 (20.09 KiB) Viewed 22 times
(ii) (iii) 100 80 60 . 40 20 100 80 60 40 20 0 7 8 9 10 11 12 Period (t) 6 7 8 Period (t) 9 10 11 12
(iv) 100 80 60 40 20 Select your answer What type of Period (t) 10 11 12 exists in the data? Is there an indication of a seasonal pattern? Qtri + (c) Compute the quarterly forecasts for next year. Year Quarter Ft 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 cuarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qira-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). Qtr2+ Qtr3
(b) Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtri = 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). ŷ = Qtri + Qtr2 + Qtr3 (c) Compute the quarterly forecasts for next year. Year Quarter Ft 4 4 4 1 2 3 4