Here Is a Set of Nested Models $\text { Dependent Variable: Respondent's Income in 1000's }$

Here is a set of nested models:
$\text { Dependent Variable: Respondent's Income in 1000's }$

$\begin{array}{lcc}\text { Independent Variable } & \text { Model } 1 & \text { Model } 2 \\\text { Education in years } & 3.57^{* * *} & -3.57^{* * *} \\\text { Education Squared } & --- & .28 * * * \\\text { Hours Worked } & .71^{* * *} & .71^{* * *} \\\text { Constant } & -38.60 & 2.55 \\\text { R-Squared } & .20 & .30\end{array}$

A) Because the linear effect does not lose its statistical significance in Model 2, the linear model is the better model.
B) Neither the linear model nor the non-linear model is better.
C) Because the squared slope is statistically significant, the non-linear model is the better model.
D) Because the R-squared for the non-linear model is much higher than the R-squared for the linear model, the non-linear model is the better model.