What is the process of predicting outcomes using relationships in data called?

The process of predicting outcomes using relationships in data is referred to as regression. Regression analysis is a statistical method used to examine the relationship between one or more independent variables and a dependent variable. It allows us to understand how the dependent variable changes as the independent variables vary, enabling predictions about future outcomes based on historical data.

For instance, if you have data on how various factors like advertising spend and economic conditions affect sales, regression can help forecast future sales based on those other metrics. This makes regression a powerful tool for decision-making and strategic planning in various fields, including business, economics, and social sciences.

Correlation, while related, specifically measures the strength and direction of a relationship between two variables without implying causation or allowing for predictions. Causation implies a directional influence—showing that one event is the result of another—but does not address the broader predictive modeling aspect inherent to regression. Data analysis is a broader term that encompasses various techniques and processes for examining data but does not specifically refer to the predictive framework that regression provides.