The data below shows the number of absences for a random sample of high school students and their final grade in a mathematics class: Absences 0 2 5 6 7 9 1 1 2 4 9 8 5 7 Grade 89.1 83.4 73.8 64.4 71.8 66.1 85 85 82.3 77.5 65.2 56 71.8 66.8 A. If we want to predict the final grade of a student based on this student's number of absences, which variable is the explanatory variable and which is the response variable? Absences is the explanatory variable and Grade " is the response variable. B. Use technology to find the linear prediction equation. Round your answers to 4 decimal places. y = 75.6818 x + 4.4545 C. Use the equation of part B to predict the final grade of a student who was absent 3 times. Predicted grade = 7 Round your answer to 2 decimal places. D. Use the equation of part B to predict the final grade of a student who was absent 5 times. If the prediction is not appropriate, enter NONE. Predicted grade = 74.82 Round your answer to 2 decimal places. E. Use the equation of part B to predict the final grade of a student who was absent 12 times. If the prediction is not appropriate, enter NONE. Predicted grade = 63.77 Round your answer to 1 decimal place. F. What is the slope of the regression line? Also provide a suitable interpretation for the slope. Slope = - Round your answer to 4 decimal places. Choose an appropriate interpretation of the slope of the regression line. If the final grade increases by 1, the number of absences is predicted to increase by about 3.0. If the final grade increases by 1, the number of absences is predicted to decrease by about 3.0. If the number of absences increases by 1, the final grade is predicted to increase by about 3.0. If the number of absences increases by 1, the final grade is predicted to decrease by about 3.0. G. What is the y-intercept of the regression line? Also provide a suitable interpretation for the intercept, if appropriate. If not appropriate, explain why not. Intercept = 82.7202 Round your answer to 4 decimal places. Choose an appropriate interpretation of the y-intercept of the regression line. If the interpretation is not appropriate, explain why not. It is not appropriate to interpret the intercept in this case because the number of absences of 0 is outside the scope of the model. If a student did not miss any classes, the final grade is predicted to be 88.1. The average final grade of a student is predicted to be 88.1. It is not appropriate to interpret the intercept in this case because the final grade of of zero is outside the scope of the model