The coefficient of determination, $R^2$, is calculated as the square of the correlation coefficient, $r$.

Given: $r = 0.969$

To compute $R^2$: $R^2 = r^2 = (0.969)^2 = 0.9391$

Convert to percentage: $R^2 = 0.9391 \times 100 = 93.9\%$

Final Answer:

The coefficient of determination, $R^2$, is 93.9%.

Would you like to proceed to part (b) or need further clarification?

Here are 5 related questions to deepen your understanding:

1. What does the coefficient of determination tell us about the data?
2. How is the correlation coefficient interpreted in the context of a regression model?
3. How can outliers affect the correlation and determination coefficients?
4. What are the assumptions behind least-squares regression?
5. How does $R^2$ differ from adjusted $R^2$?

Tip: A high $R^2$ value indicates a strong linear relationship, but always check residual plots to confirm the model's validity!