What category of data involves more than two variables?

Multivariate data is characterized by the involvement of multiple variables, typically more than two. In statistical analysis, such data allows researchers to explore relationships and interactions between several variables simultaneously, making it powerful for understanding complex phenomena. By examining multivariate datasets, analysts can uncover patterns, correlations, and trends that would not be visible when looking at a single variable or even two variables in isolation. This approach is commonly employed in various fields, including social sciences, marketing, and medical research, where many factors influence outcomes.

The other options, while related to data representation or analysis, do not inherently involve multiple variables in the same way that multivariate data does. For instance, box plots are used to display the distribution of a single variable or to compare distributions across categories, linear regression typically analyzes the relationship between a dependent variable and one or more independent variables but is primarily associated with two or more variables specifically tied through a linear relationship, and pie charts represent parts of a whole for a single categorical variable. Thus, they do not fit the requirement of involving more than two variables simultaneously as multivariate data does.