To compute the standard error of the regression model (se), we follow these steps:

Step 1: Compute the Residual Sum of Squares (SSE)

The standard error of the regression is given by:

$s_e = \sqrt{\frac{SSE}{n - 2}}$

where:

• $SSE$ is the sum of squared errors (also called the residual sum of squares).
• $n$ is the number of observations.
• The errors (residuals) are the differences between the actual College GPA and the predicted College GPA.

Step 2: Compute Predicted College GPAs

Using the regression equation:

$\hat{Y} = 2.96 + 0.093(X)$

where $X$ is the High School GPA, and $\hat{Y}$ is the predicted College GPA.

Now, upload the full dataset so I can perform the calculations and find the standard error for you.