https://file.aiquickdraw.com/mathbot/file/1730352705409vrqhct6t.jpg

a)Construct a scatter diagram for these data. 

b)Calculate the correlation of coefficient (***r***). Interpret the results. Find the regression equation.

c)Plot a regression line on the scatter diagram of part (a).

d)Predict the final grade with 10 number of absences.

### 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?

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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.

a) Construct a scatter diagram for these data.

b) Calculate the correlation coefficient (r). Interpret the results. Find the regression equation.

c) Plot a regression line on the scatter diagram of part (a).

d) Predict the final grade with 10 number of absences.

Learn how to construct a scatter plot, calculate the correlation coefficient, and perform regression analysis for student grade prediction based on absences. This problem includes creating a scatter diagram, interpreting correlation, plotting a regression line, and predicting grades.

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