What is a common use of a scatter plot in data analysis?

A scatter plot is commonly used to identify correlations between variables because it visually represents the relationship between two quantitative variables. Each point on a scatter plot corresponds to an observation in the dataset, with one variable plotted along the x-axis and the other along the y-axis. By examining the pattern and direction of the points, analysts can determine if there is a positive correlation (where one variable increases as the other increases), a negative correlation (where one variable increases as the other decreases), or no correlation at all. This visual representation helps in understanding how changes in one variable may be associated with changes in another, which is essential for predictive analysis and decision-making.

In contrast, other options do not align with the primary function of a scatter plot. For example, displaying categorical data is typically done using bar charts or pie charts, which are better suited to illustrate frequencies or proportions of categories. Presenting percentages of a whole also does not apply, as scatter plots are not designed for that purpose; they focus on the relationship between two numerical variables instead. Sorting data in order would be managed through a different process, such as sorting functions in a data table, rather than through a visual scatter plot.