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 5-year period from 2008 to 2012.The following is the resulting regression equation: log 10 = 6.102 + 0.012 X - 0.129 Q 1 - 0.054 Q 2 + 0.098 Q 3 where is the estimated number of contracts in a quarter X is the coded quarterly value with X = 0 in the first quarter of 2008 Q 1 is a dummy variable equal to 1 in the first quarter of a year and 0 otherwise Q 2 is a dummy variable equal to 1 in the second quarter of a year and 0 otherwise 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 constant 6.102 in the regression equation is A)the fitted value for the first quarter of 2008,prior to seasonal adjustment,is log₁₀(6.102). B)the fitted value for the first quarter of 2008,after to seasonal adjustment,is log₁₀(6.102). C)the fitted value for the first quarter of 2008,prior to seasonal adjustment,is 10^6.102. D)the fitted value for the first quarter of 2008,after to seasonal adjustment,is 10^6.102.

Question

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The constant in a regression equation represents the intercept, which is the expected value of the dependent variable when all predictors are zero. In this context, it indicates the fitted value prior to seasonal adjustment, expressed as 10 raised to the power of the constant.

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