To Decide Whether $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X + u _ { i } \text { or } \ln \left( Y _ { i } \right) = \beta _ { 0 } + \beta _ { 1 } X + u _ { i }$

To decide whether $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X + u _ { i } \text { or } \ln \left( Y _ { i } \right) = \beta _ { 0 } + \beta _ { 1 } X + u _ { i }$
fits the data better, you cannot consult the regression R² because

A) ln (Y) may be negative for 0<Y<1 .
B) the TSS are not measured in the same units between the two models.
C) the slope no longer indicates the effect of a unit change of X on Y in the log-linear model.
D) the regression $R ^ { 2 }$ can be greater than one in the second model.