From the regression output,the predicted regression line is:
RY

\begin{array}{l}\hline \text { gression Statistics }\\\begin{array}{lr}\hline\text { R } & 0.971 \\\text { e} & \mathbf{A} \\\text { R-Square }& \mathbf{B} \\\text { Error } &30.462\\\text { ions } & 51\\\hline\end{array}\end{array}

\begin{array}{rrrrrr}\hline&\text { df } & {\text { SS }} & {\text { MS }} & \boldsymbol{F} & {\text { Signiticance F}} \\\hline\text { on }& \text { C } & 747851.57 & 373925.79 & 402.98 & 9.89 \mathrm{E}-31 \\&48 & \text { D } & 927.91 & & \\&50 & 792391.11 & & & \\\hline\end{array}

\begin{array}{lrrrrrr}\hline &\text { Coefficients } & \begin{array}{c}\text { Standard } \\\text { Error }\end{array} &{\text { t Stat }} & \text { P-value } & \text { Lower 95\% } & \text { Upper 95\% } \\\hline &\mathbf{E} & 62.13 & 26.79 & 1.60 \mathrm{E}-30 & 1539.66 & 1789.51 \\\text { Roses }&-6.68 & \mathbf{F} & -1.41 & 1.64 \mathrm{E}-01 & -16.16 & 2.81 \\\text { le Income }&9.73 & 0.34 & \mathbf{G} & 1.23 \mathrm{E}-31 & 9.04 & 10.42\\\hline\end{array}

A) Q_R^d = 1664.46 ? 6.68P_R + 9.73M.
B) P_R = 1664.46 ? 6.68Q_R + 9.73M.
C) Q_R^d = 2.32 ? 6.68P_R + 9.73M.
D) There is not sufficient information to answer the question.

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