Rafael claims it's easiest to score points when a scatterplot has a linear association. Sahana says it's easiest when a scatterplot has a strong association. Who is correct?
Both Rafael and Sahana are partially correct.
Rafael is correct in that it can be easier to score points when a scatterplot has a linear association. This is because when the points in a scatterplot are more closely aligned along a straight line, it becomes easier to estimate the values of the dependent variable (y) based on the independent variable (x). This gives a clearer trend and makes it easier to make accurate predictions or draw conclusions.
On the other hand, Sahana is also correct in stating that it is easier to score points when a scatterplot has a strong association. When there is a strong association in a scatterplot, it means that the points are closely clustered together and have a clear trend, whether it is linear, exponential, quadratic, or any other form. In such cases, it becomes easier to identify patterns, draw conclusions, and predict values accurately.
Overall, both Rafael and Sahana are correct, but Rafael's statement specifically applies to scatterplots with a linear association, while Sahana's statement applies more generally to scatterplots with a strong association of any form.