What is a scatter plot primarily used for in data analysis?

A scatter plot is primarily used to visualize the relationship between two quantitative variables. This graphical representation helps in understanding how one variable may affect or correlate with another. Plotting each pair of values as a point on a two-dimensional graph allows analysts to quickly discern patterns, trends, or potential anomalies in the relationship.

In addition, scatter plots can reveal the nature of the relationship (linear, nonlinear, or no relationship) and the strength of the correlation, which is particularly useful in data-driven decision-making processes. Each point on the scatter plot corresponds to an observation in the dataset, enabling you to see points of interest, such as clusters or outliers, that could further inform analysis.

While the other options suggest important functions of data analysis—like comparing categorical data or summarizing findings—they do not accurately describe the specific focus and utility of scatter plots in visualizing quantitative variables. Scatter plots excel in providing insights into the interactions between two numerical datasets, making them an essential tool in fields such as statistics, business analytics, and research.