The least-squares regression line, treating the number of absences $x$ as the explanatory variable and the final grade $y$ as the response variable, is given by:

$\hat{y} = 89.304 - 3.076x$

Where $\hat{y}$ is the predicted final grade, and $x$ is the number of absences.

Do you need any further details or have any questions?

Here are five related questions:

1. How can you interpret the slope of the regression line?
2. What does the intercept represent in this context?
3. How would you predict the grade for a student with 5 absences using this model?
4. What is the significance of the $R^2$ value in this regression analysis?
5. How can outliers affect the least-squares regression line?

Tip: Always check the residuals to assess how well your regression model fits the data!