Final answer:
Simple linear regression analysis enables prediction of response values based on predictor values and goes beyond just the strength of association measurable by correlation analysis. So the correct answer is option B.
Step-by-step explanation:
Doing a simple linear regression analysis allows you to predict a value of the response given a value of the predictor. This involves deciding which variable should be the independent variable and which should be the dependent variable. Then, you can draw a scatter plot of the data and calculate the least-squares line, putting the equation in the form ý = a + bx. Additionally, you can find the correlation coefficient to determine the strength and direction of the linear relationship between the variables.
If the correlation coefficient is significant, it suggests a meaningful linear relationship, and you can use the regression equation for making predictions. For example, given a person's age, you could predict their height, or given the number of miles from campus, you could predict the total cost of supplies.
Overall, simple linear regression analysis provides additional insights compared to correlation analysis, by not only revealing the strength of an association but also allowing for the prediction of specific values and the modeling of the relationship between variables.