Final answer:
A scatterplot that shows a strong association between two variables despite a correlation near zero typically displays a non-linear pattern. Regular linear regression would not be suitable, and a statistician would use curve fitting or non-linear regression techniques to model the relationship accurately.
Step-by-step explanation:
To identify a scatterplot that shows a strong association between two variables despite a correlation probably near zero, one needs to understand that correlation measures the strength and direction of a linear relationship between two variables. A scatterplot with a non-linear pattern, such as a parabolic or circular shape, could display a strong relationship between variables even though the correlation coefficient might be near zero because the correlation coefficient only measures linear associations.
Typically, if data in a scatterplot form a distinct pattern that is not a straight line, the association is non-linear, and thus regular linear regression is not the best method to describe the relationship. In such cases, curve fitting or non-linear regression techniques are more appropriate. Examples of non-linear patterns include U-shaped, exponential, and sinusoidal. A statistician would perform curve fitting to more accurately model these types of relationships.
For the mentioned scatterplot showing a correlation coefficient of r = 0.55, a curve might be a better fit. The presence of a moderate correlation coefficient suggests a relationship, but the pattern suggests a linear model might not be the best fit, so the data could be better represented by a curve.