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TABLE 16-12 A Local Store Developed a Multiplicative Time-Series Model to Forecast

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TABLE 16-12
A local store developed a multiplicative time-series model to forecast its revenues in future quarters, using quarterly data on its revenues during the 4-year period from 2005 to 2009. The following is the resulting regression equation:
log₁₀ TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters, using quarterly data on its revenues during the 4-year period from 2005 to 2009. The following is the resulting regression equation: log₁₀   = 6.102 + 0.012 X - 0.129 Q₁ - 0.054 Q₂ + 0.098 Q₃ where   is the estimated number of contracts in a quarter. X is the coded quarterly value with X = 0 in the first quarter of 2005. Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise. Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise. Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise. -Referring to Table 16-12, the best interpretation of the coefficient of Q₂ (-0.054)  in the regression equation is A)  the revenues in the second quarter of a year is approximately 5.4% lower than the average over all 4 quarters. B)  the revenues in the second quarter of a year is approximately 5.4% lower than it would be during the fourth quarter. C)  the revenues in the second quarter of a year is approximately 11.69% lower than the average over all 4 quarters. D)  the revenues in the second quarter of a year is approximately 11.69% lower than it would be during the fourth quarter. = 6.102 + 0.012 X - 0.129 Q₁ - 0.054 Q₂ + 0.098 Q₃
where TABLE 16-12 A local store developed a multiplicative time-series model to forecast its revenues in future quarters, using quarterly data on its revenues during the 4-year period from 2005 to 2009. The following is the resulting regression equation: log₁₀   = 6.102 + 0.012 X - 0.129 Q₁ - 0.054 Q₂ + 0.098 Q₃ where   is the estimated number of contracts in a quarter. X is the coded quarterly value with X = 0 in the first quarter of 2005. Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise. Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise. Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise. -Referring to Table 16-12, the best interpretation of the coefficient of Q₂ (-0.054)  in the regression equation is A)  the revenues in the second quarter of a year is approximately 5.4% lower than the average over all 4 quarters. B)  the revenues in the second quarter of a year is approximately 5.4% lower than it would be during the fourth quarter. C)  the revenues in the second quarter of a year is approximately 11.69% lower than the average over all 4 quarters. D)  the revenues in the second quarter of a year is approximately 11.69% lower than it would be during the fourth quarter. is the estimated number of contracts in a quarter.
X is the coded quarterly value with X = 0 in the first quarter of 2005.
Q₁ is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise.
Q₂ is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise.
Q₃ is a dummy variable equal to 1 in the third quarter of a year and 0 otherwise.
-Referring to Table 16-12, the best interpretation of the coefficient of Q₂ (-0.054) in the regression equation is


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