Refer to the Following Nonlinear Model Which Relates W to P

Refer to the following nonlinear model which relates W to P, Q, and R:
$W = a P ^ { b } Q ^ { c } R ^ { d }$
The computer output form the regression analysis is:
$$\begin{array} { r l l l l l } \text { DEPENDENTVARIAELE: } & \text { LNW } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ONF } \\\text { OESERVATIONS: } & \text { 19 } & \mathbf { 0 . 9 0 2 3 } & 43.12 & \mathbf { 0 . 0 0 0 1 } & \\\\& \text { PARAMETER } & \text { STANDARD } & & \\\text { VARIAELE } & \text { ESTMMATE } & \text { ERROR } & \text { T-RATIO } & \text { P-VALUE } \\\text { INTERCEPT } & \mathbf { 2 . 5 0 } & \mathbf { 0 . 4 5 } & \mathbf { 5 . 5 6 } & \mathbf { 0 . 0 0 0 1 } \\\text { LNP } & - 5.10 & 1.75 & - \mathbf { 2 . 9 1 } & \mathbf { 0 . 0 1 1 3 } \\\text { LNQ } & 12.4 & \mathbf { 3 . 2 } & \mathbf { 3 . 8 3 } & \mathbf { 0 . 0 0 1 7 } \\\text { LNR } & \mathbf { - 6 . 0 0 } & 1.5 & - 400 & \mathbf { 0 . 0 0 1 0 }\end{array}$$
-The value of R² tells us that

A) 0.9023% of the total variation in ln W is explained by the regression equation.
B) 90.23% of the total variation in ln W is explained by the regression equation.
C) 0.9023% of the total variation in P, W, and R is explained by the regression equation.
D) 0.9023% of the total variation in ln P, ln Q, and ln R is explained by the regression equation.