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The coefficient of determination is:

2.786

0.701

0.679

The coefficient of determination, denoted as \( R^2 \), is a measure of the proportion of the variance in the dependent variable (here, "grade") that is predictable from the independent variables.

From the provided output, the value of \( R^2 \) is given as 70.1%.

To report it as a decimal, the value is:

\[
R^2 = 0.701
\]

Thus, the correct value of the coefficient of determination is **0.701**.

Would you like more details or have any questions?

Here are 5 related questions to explore further:
1. What does the adjusted \( R^2 \) indicate?
2. How is the degrees of freedom calculated in this context?
3. What is the difference between \( R^2 \) and adjusted \( R^2 \)?
4. What does the value of \( s = 2.786 \) represent in the output?
5. Why is the coefficient of determination important in regression analysis?

**Tip**: Always compare \( R^2 \) and adjusted \( R^2 \) to understand the impact of additional predictors on the model's performance.

Report the value of the coefficient of determination.

Learn how to report the coefficient of determination (R²) from a regression analysis. The problem involves calculating the R² value, understanding its significance in regression analysis, and the differences between R² and adjusted R². Suitable for undergraduate students.

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