Which analysis technique helps understand the relationship between variables?

Regression analysis is the technique that is specifically designed to understand the relationships between variables. It allows analysts to identify how the dependent variable changes when one or more independent variables are varied. Through regression, you can determine not just if a relationship exists, but also how strong that relationship is, and the nature of the relationship—whether it's linear or nonlinear.

In regression analysis, you might work with different forms such as simple linear regression, which involves two variables, or multiple regression, which includes multiple independent variables. This capability makes regression particularly powerful for predictive modeling, allowing stakeholders to forecast outcomes based on the given input variables.

The other techniques have distinct purposes: clustering analysis focuses on grouping data points based on similarity rather than exploring relationships, time series analysis is aimed at understanding patterns over time rather than relationships between different variables, and sentiment analysis is concerned with extracting and quantifying opinions from text data. Thus, while all these techniques have their uses, regression analysis is the primary method for exploring and understanding variable relationships within data.