a) The correlation coefficient between the number of absences and final grade is a strong negative -0.991. This means that there's a high likelihood that the number of absences is affecting the final grade, but not in a good way.
b) The slope of the regression line, which helps us understand the relationship between absences and final grade, is -0.624. This indicates that on average, a student's final grade is expected to decrease by 0.624 points for every additional absence they have.
c) The standard error for the model, which measures the difference between the actual exam scores and the predicted scores, is 2.455. This is a measure of the model's accuracy, and the lower the standard error, the more accurate the model is.
d) Yes, there is a significant correlation between the number of absences and exam score. The negative correlation coefficient tells us that the more absences a student has, the lower their exam score is likely to be.
e) Based on the regression line, we can estimate that a student with 7 absences is expected to receive an exam score of 83 (rounded to the nearest whole number). However, it's important to note that this is just an estimate, and other factors such as the student's study habits, test-taking skills, and prior knowledge of the subject could also influence their final grade.