A statistics instructor wants to use the number of hours a student studies for an exam to predict exam scores for his class. He collected the data from his previous classes which showed that the average time spent studying for a final exam in statistics is 8.5 hours, with a standard deviation of 1.5 hours and the average exam score is 75%, with a standard deviation of 15%. Based on these data, the correlation coefficient () is 0.76. In his current class there is a student who studied 10 hours for the final exam. Do you think that the instructor should use linear regression to predict exam scores for this student? Choose the best answer below
A.
Yes, linear regression could be appropriate if the scatterplot shows a clear linear relationship
B.
Yes, r = 0.76 indicates that there is a strong positive linear correlation, so linear regression is an appropriate approach.
C.
Yes, because linear regression is the statistical method used to make predictions when you have bivariate quantitative data
D.
No, because there is no way to prove that more hours of study causes higher exam scores
B.
Yes, r = 0.76 indicates that there is a strong positive linear correlation, so linear regression is an appropriate approach.