In multivariate analysis, what is typically evaluated?

In multivariate analysis, the primary focus is on examining the relationships among multiple variables simultaneously. This approach allows analysts to understand how different variables interact with each other and to identify patterns or dependencies that may not be apparent when analyzing individual variables separately. By evaluating these relationships, data analysts can uncover insights into complex datasets, leading to more informed decision-making.

Analyzing trends over time is more relevant to univariate or time series analysis, where the emphasis is on a single variable across a timeline. The variability of a single variable specifically targets the distribution and dispersion of that one variable, which does not encompass the interactions among multiple variables. The differentiation between datasets might involve comparing one dataset against another, but it does not delve into the relationships among several variables within those datasets. Therefore, the emphasis on understanding how multiple data points relate to each other distinctly identifies the purpose of multivariate analysis.