What is a scatterplot primarily used for?

A scatterplot is primarily designed to visualize the relationship between two quantitative variables. By plotting individual data points on a two-dimensional graph, where one variable is represented on the x-axis and the other on the y-axis, one can observe how changes in one variable may be associated with changes in the other. This visual representation makes it easier to identify patterns, correlations, or trends within the data.

For example, if you were investigating the relationship between hours studied and exam scores, a scatterplot would allow you to see whether more study hours tend to correlate with higher scores. The plotted points can reveal various characteristics of the relationship, such as linearity or the presence of clusters.

While scatterplots can help identify potential outliers indirectly by showing points that are distant from others, their primary function is not explicitly outlier analysis. Instead, outliers may be more thoroughly examined using specific statistics or other types of visualizations designed for that purpose. The other options discussed do not align with the main purpose of a scatterplot, as it does not deal with categorical variables directly, calculate means, or analyze specific outliers in isolation.