Final answer:
The prediction of an individual’s score on the response variable based on our knowledge of this individual’s value on the explanatory variable will be more accurate when the correlation between the two variables is strong.
Step-by-step explanation:
The prediction of an individual’s score on the response variable based on our knowledge of this individual’s value on the explanatory variable will be more accurate when the correlation between the two variables is strong.
A strong correlation (close to 1 or -1) indicates a stronger relationship between the variables, meaning that changes in one variable are highly predictable as the other variable changes. On the other hand, a weak correlation (close to 0) suggests that the relationship between the variables is less predictable and the accuracy of predictions is lower.
For example, if there is a strong positive correlation between studying hours and test scores, knowing a student's studying hours will allow us to predict their test score with higher accuracy. On the other hand, if the correlation is weak, the accuracy of the prediction will be lower.