Which graph would best represent data where the goal is to find numerical relationships among several variables?

To determine the graph that best represents data aimed at finding numerical relationships among several variables, it's important to consider what each graph type is designed to illustrate.

A heatmap is particularly effective for showing the relationships between multiple variables because it uses color coding to represent different values or intensities across a two-dimensional space. This allows for quick visual assessment of patterns, correlations, and interactions among the variables. For instance, in a heatmap, the intensity of color can indicate the strength or weakness of relationships, making it easier to identify clusters of data or specific areas of interest where variables might be significantly correlated.

Other graph types serve different purposes. A scatter plot displays the relationship between two continuous variables and is useful for identifying trends, but it does not naturally extend to displaying relationships across multiple variables in a single view. While box plots summarize data distribution and highlight outliers, they do not directly reveal relationships among several variables. Pie charts illustrate proportions of a whole but are not suitable for displaying relationships between variables, as they do not provide any information on correlation or interaction.

Thus, the heatmap is the most appropriate choice for analyzing numerical relationships among several variables, as it offers a comprehensive and visually intuitive way to understand complex interactions within the data.