The estimated exam mark can be found using the formula for a multiple linear regression model:

$\text{Estimated Exam Mark} = \text{Intercept} + (\text{Attendance Coefficient} \times \text{Number of Classes}) + (\text{Hours of Paid Work Coefficient} \times \text{Hours of Paid Work})$

From the given dataThe estimated exam mark can be found using the formula for a multiple linear regression model:

$\text{Estimated Exam Mark} = \text{Intercept} + (\text{Attendance Coefficient} \times \text{Number of Classes}) + (\text{Hours of Paid Work Coefficient} \times \text{Hours of Paid Work})$

From the given

• Intercept = 10.11
• Attendance Coefficient = 4.24
• Hours of Paid Work Coefficient = -0.02
• Number of Classes = 7
• Hours of Paid Work = 8

Substituting these values into the formula:

$\text{Estimated Exam Mark} = 10.11 + (4.24 \times 7) + (-0.02 \times 8)$

Now let's compute this.

$\text{Estimated Exam Mark} = 10.11 + 29.68 - 0.16 = 39.63$

The estimated exam mark is 39.63.

Do you want more details or have any questions?

Here are 5 related questions to expand on this:

1. What does the Multiple R value indicate in this regression output?
2. How does the R square value relate to the accuracy of the model?
3. What role do standard errors play in regression analysis?
4. What does a negative coefficient, such as for hours of paid work, imply in this context?
5. How would the estimated exam score change if the student attended more classes but worked the same number of hours?

Tip: In regression analysis, coefficients represent the change in the dependent variable for a one-unit change in the independent variable, holding all else constant.