The Linear Regression Equation, Y = a + BX, Was

The linear regression equation, Y = a + bX, was estimated. The following computer printout was
obtained:
$\begin{array}{rllll}\text { DEPENDENT VARIABLE: } & \text { Y } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ON F } \\\text { OBSERVATIONS: } &18 & 0.3066 & 7.076 & 0.0171\end{array}$

$\begin{array}{lllll}\text { VARIABLE } & \begin{array}{llll}\text { PARAMETER } \\\text { ESTIMATE }\end{array} & \begin{array}{l}\text { STANDARD } \\\text { ERROR }\end{array} & \text { T-RATIO } & \text { P-VALUE } \\\text { INTERCEPT } & 15.48 & 5.09 & 3.04 & 0.0008\\X&-21.36&8.03&-2.66&0.0171\end{array}$
-The value of the R² statistic indicates that

A) 0.3066% of the total variation in Y is explained by the regression equation.
B) 0.3066% of the total variation in X is explained by the regression equation.
C) 30.66% of the total variation in Y is explained by the regression equation.
D) 30.66% of the total variation in X is explained by the regression equation.