A statistics student wants to determine if there is a relationship between a student’s number of absences, x, and their grade point average (GPA), y. The given data lists the number of absences and GPAs for 15 randomly selected students. A 2-column table with 15 rows. Column 1 is labeled Number of Absences with entries 15, 1, 0, 6, 9, 12, 3, 3, 1, 2, 7, 0, 4, 9, 10. Column 2 is labeled G P A with entries 2.1, 4.3, 4.5, 3.2, 4.0, 1.7, 3.8, 2.9, 3.6, 3.4, 2.6, 3.1, 2.8, 2.8, 4.1.

Using technology, the slope of the least-squares regression line is

–0.10, which means for each additional absence, the GPA will decrease by 0.10 points.
–0.10, which means for each additional absence, the GPA is predicted to decrease by 0.10 points.
0.10, which means for each additional absence, the GPA will increase by 0.10 points.
0.10, which means for each additional absence, the GPA is predicted to increase by 0.10 points.