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 y-intercept is 4.5, which means a student with no absences has a GPA of 4.5. 4.5, which means a student with no absences is predicted to have a GPA of 4.5. 3.79, which means a student with no absences is predicted to have a GPA of 3.79. 3.79, but it does not make sense to interpret the y-intercept in this context.