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Consider the Following Set of Quarterly Sales Data Given in Thousands

question 99

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Consider the following set of quarterly sales data given in thousands of dollars.The following dummy variable model that incorporates a linear trend and constant seasonal variation was used: yt = β0+ β1t + βQ1(Q1)+ βQ2(Q2)+ βQ3(Q3)+ εt
In this model there are 3 binary seasonal variables (Q1,Q2,and Q3).
Where
Qi is a binary (0,1)variable defined as:
Qi = 1,if the time series data is associated with quarter i;
Qi = 0,if the time series data is not associated with quarter i.
The results associated with this data and model are given in the following computer output.
The prediction equation is
Sales = 2442 + 6.2 Time - 693 Q1 - 1499 Q2 + 153 Q3  Predictor  Coef  StDev  T  P  Constant 2441.7151.916.080.000 Time 6.2514.700.430.684 Q1 692.9142.84.850.002 Q2 1499.2139.010.790.000 Q3 152.9136.61.120.300\begin{array} { l c c c c } \text { Predictor } & \text { Coef } & \text { StDev } & \text { T } & \text { P } \\ \text { Constant } & 2441.7 & 151.9 & 16.08 & 0.000 \\\text { Time } & 6.25 & 14.70 & 0.43 & 0.684 \\\text { Q1 } & -692.9 & 142.8 & -4.85 & 0.002 \\\text { Q2 } & -1499.2 & 139.0 & -10.79 & 0.000 \\\text { Q3 } & 152.9 & 136.6 & 1.12 & 0.300 \end{array}
S=166.4RSq=96.4%RSq(adj)=94.4%\mathrm { S } = 166.4 \quad \mathrm { R } - \mathrm { Sq } = 96.4 \% \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 94.4 \%
Analysis of Variance
 Source  DF  SS  MS  F  P  Regression 45209942130248547.060.000 Residual Error 719373327676 Total 115403675\begin{array} { l r r r c c } \text { Source } & \text { DF } & \text { SS } & \text { MS } & \text { F } & \text { P } \\ \text { Regression } & 4 & 5209942 & 1302485 & 47.06 & 0.000 \\ \text { Residual Error } & 7 & 193733 & 27676 & & \\ \text { Total } & 11 & 5403675 & & & \end{array}
-Provide a managerial interpretation of the regression coefficient for the variable "time."


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