To find the coefficient of determination $R^2$, we use the following formula:

$R^2 = \frac{\text{SS}_{\text{regression}}}{\text{SS}_{\text{total}}}$

From the table:

• $\text{SS}_{\text{regression}} = 19.7412358$
• $\text{SS}_{\text{total}} = 21.95573617$

Now, let's calculate $R^2$:

$R^2 = \frac{19.7412358}{21.95573617} \approx 0.8993$

Thus, the coefficient of determination $R^2$ is approximately 0.8993 (rounded to four decimal places).

Would you like more details or have any questions?

Here are 5 related questions:

1. What does a high $R^2$ value indicate about the model fit?
2. How do you interpret the significance $F$-value in this context?
3. How does the $MS$ value relate to the $F$-statistic?
4. Why is it important to check both $R^2$ and $F$-value in regression?
5. How would you compute adjusted $R^2$?

Tip: $R^2$ values closer to 1 indicate a strong fit between the model and the data, meaning the model explains most of the variation in the dependent variable.