Final answer:
Yes, we can still make predictions with a model even if the predictor and response are highly correlated. Correlation measures the strength and direction of the relationship between two variables, but it does not imply causation.
Step-by-step explanation:
Yes, we can still make predictions with a model even if the predictor and response are highly correlated. Correlation measures the strength and direction of the relationship between two variables, but it does not imply causation. When the predictor and response are highly correlated, it means that changes in one variable tend to be associated with changes in the other variable.
For example, let's consider a scenario where we have a high correlation between the amount of studying a student does and their test scores. Even though the two variables are highly correlated, it does not mean that studying directly causes higher test scores. However, we can still use the model to predict the test scores based on the amount of studying a student does.
When using a model with highly correlated predictor and response variables, it is important to note that the model may not be the best representation of the relationship. It is always advisable to examine the scatter plot and consider other modeling techniques to determine the most appropriate model.