Using data from 50 workers, a researcher estimates wage \( =\theta_{0}+\theta_{1} \) Education \( +B_{2} \) Experience \( +B_{3} A_{B e}+\varepsilon \), where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. A portion of the regression results is shown in the following table.
\begin{tabular}{lcc|c|c|c|}
\hline & Coefficients & Standard Error & \( t \) Stat & \( p \)-Value \\
\hline Intercept & 8.23 & 4.40 & 1.87 & 0.0678 \\
Education & 1.23 & 0.38 & 3.24 & 0.0022 \\
Experience & 0.53 & 0.18 & 2.94 & 0.0051 \\
Age & -0.08 & 0.07 & -1.14 & 0.2590 \\
\hline
\end{tabular}
a-1. Interpret the estimated coefficients for Education.
As Education increases by 1 year, Wage is predicted to increase by \( 1.23 / \) hour.
As Education increases by 1 year, Wage is predicted to increase by \( 0.53 / \) hour.
As Education increases by 1 year, Wage is predicted to increase by \( 1.23 / \) hour, holding Age and Experience constant.
As Education increases by 1 year, Wage is predicted to increase by \( 0.53 / \) hour, holding Age and Experience constant.
a-2. Interpret the estimated coefficients for Experience.
As Experience increases by 1 year, Wage is predicted to increase by 1.23/hour.
As Experience increases by 1 year, Wage is predicted to increase by \( 0.53 / \) hour.
As Experience increases by 1 year, Wage is predicted to increase by \( 1.23 / \) hour, holding Age and Education constant.
As Experience increases by 1 year, Wage is predicted to increase by \( 0.53 / \) hour, holding Age and Education constant.
