Refer to the Following Computer Output from Estimating the Parameters

Refer to the following computer output from estimating the parameters of the nonlinear model
$$\begin{array} { r l l l l l } \text { DEPENDENT VARIAELE: } & \text { LNY } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ONF } \\\text { OESERVATIONS: } & \text { 32 } & \mathbf { 0 . 7 7 5 6 } & \mathbf { 3 2 . 4 4 } & \mathbf { 0 . 0 0 0 1 } & \\\\& \text { PARAMETER } & \text { STANDARD } & & \\\text { VARIAELE } & \text { ESTMATE } & \text { ERROR } & \text { T-RATIO } & \text { P-VALUE } \\\text { INTEREEPT } & \mathbf { - 0 . 5 9 3 1 } & \mathbf { 0 . 3 2 } & \mathbf { - 2 . 1 7 } & \mathbf { 0 . 0 3 9 0 } \\\text { LNRR } & \mathbf { 4 . 6 5 } & 1.36 & \mathbf { 3 . 4 3 } & \mathbf { 0 . 0 0 1 9 } \\\text { LNS } & \mathbf { - 0 . 4 4 } & \mathbf { 0 . 2 4 } & - 1.83 & \mathbf { 0 . 0 7 7 4 } \\\text { LNT } & \mathbf { 8 . 2 8 } & \mathbf { 4 . 6 } & \mathbf { 1 . 8 0 } & \mathbf { 0 . 0 8 2 6 }\end{array}$$
-The nonlinear relation can be transformed into the following linear regression model:

A) $Y = \ln \left( a R ^ { b } S ^ { c } T ^ { d } \right)$
B) $\ln Y = \ln \left( a R ^ { \mathrm { b } } S ^ { \mathrm { c } } T ^ { \mathrm { d } } \right)$
C) $\ln Y = \ln a \cdot \ln R \cdot \ln S \cdot \ln T$
D) $\ln Y = \ln a + b \ln R + c \ln S + d \ln T$