2. The instructor of a mathematics class collected data to see whether there is a correlation between the number of absences (X) and the student's score on the final exam (Y). The number of absences and score on the final exam were recorded. The following regression equation was obtained: Final score = 92.5317 – 3.7611 (Absences) a) (5 points) What would be the predicted final exam score for a student that had 10 absences? b) (5 points) The student from part (a) actually scored a 60 on the final exam. What is the residual for this student? Show all work.

Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter7: Distance And Approximation
Section7.3: Least Squares Approximation
Problem 31EQ
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2. The instructor of a mathematics class collected data to see whether there is a correlation
between the number of absences (X) and the student's score on the final exam (Y). The
number of absences and score on the final exam were recorded. The following regression
equation was obtained: Final score = 92.5317 – 3.7611 (Absences)
a) (5 points) What would be the predicted final exam score for a student that had 10
absences?
b) (5 points) The student from part (a) actually scored a 60 on the final exam. What is the
residual for this student? Show all work.
Transcribed Image Text:2. The instructor of a mathematics class collected data to see whether there is a correlation between the number of absences (X) and the student's score on the final exam (Y). The number of absences and score on the final exam were recorded. The following regression equation was obtained: Final score = 92.5317 – 3.7611 (Absences) a) (5 points) What would be the predicted final exam score for a student that had 10 absences? b) (5 points) The student from part (a) actually scored a 60 on the final exam. What is the residual for this student? Show all work.
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