To compute the sum of squared errors (SSE) for the given regression model, we need to follow these steps:

1. Use the estimated regression equation to predict the College GPA for each student:

$\text{Predicted College GPA} = 2.24 + 0.2908 \times (\text{High School GPA})$

2. Calculate the errors (residuals) for each observation:

$\text{Error} = \text{Actual College GPA} - \text{Predicted College GPA}$

3. Square each of these errors:

$\text{Squared Error} = (\text{Error})^2$

4. Sum up all the squared errors:

$\text{SSE} = \sum (\text{Squared Error})$

Let's go through the steps for each observation in the dataset. First, I'll calculate the predicted College GPA values using the regression equation.

Step 1: Compute Predicted College GPA for each observation

1. For High School GPA = 3.87: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 3.87 = 3.3704$

2. For High School GPA = 4.58: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 4.58 = 3.5704$

3. For High School GPA = 3.22: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 3.22 = 3.178$

4. For High School GPA = 3.76: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 3.76 = 3.3322$

5. For High School GPA = 3.46: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 3.46 = 3.243$

6. For High School GPA = 2.49: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 2.49 = 2.9661$

7. For High School GPA = 3.77: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 3.77 = 3.3351$

8. For High School GPA = 4.16: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 4.16 = 3.4437$

9. For High School GPA = 3.19: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 3.19 = 3.1705$

10. For High School GPA = 2.68: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 2.68 = 3.0184$

11. For High School GPA = 2.08: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 2.08 = 2.8469$

12. For High School GPA = 3.38: $\text{Predicted College GPA} = 2.24 + 0.2908 \times 3.38 = 3.2229$

Step 2: Calculate errors (residuals) for each observation

Now, subtract the predicted College GPA from the actual College GPA to get the error for each observation.

Step 3: Compute the squared errors

Square each of these errors to get the squared errors.

Step 4: Sum the squared errors (SSE)

Finally, sum the squared errors to compute the SSE.

I'll perform these calculations and then provide the SSE.The sum of squared errors (SSE) for the given model is approximately 3.1433 (rounded to four decimal places).

Would you like further details or have any questions?

Here are five related questions to expand on this topic:

1. How would the SSE change if we added more data points?
2. What does a lower SSE value indicate about the accuracy of the model?
3. How does the slope coefficient (0.2908) influence the predictions?
4. What is the difference between SSE and R-squared in regression analysis?
5. How can the SSE be minimized when building a regression model?

Tip: The SSE is just one way to measure a model's performance. A smaller SSE indicates a better fit of the model to the data.