Part (c): Scatter Diagram with Regression Line

The scatter diagram now includes the regression line, which represents the best-fit line for predicting final grades based on the number of absences.

Part (d): Predicted Final Grade for 10 Absences

Using the regression equation, the predicted final grade for a student with 10 absences is approximately 66.27.

Would you like further details on any of these steps or calculations?

Here are some related questions to explore further:

1. How does the value of $r$ change if a new student with no absences and a perfect grade is added?
2. What are the implications of a strong negative correlation in practical scenarios?
3. How would the regression line adjust if a student with a very low grade and high absences were removed?
4. Can we predict with certainty a student's grade based on their number of absences alone?
5. How can outliers affect the calculation of the correlation coefficient?

Tip: When interpreting correlation, remember that correlation does not imply causation; other factors might influence the results.