What is the goal of applying regression models in data analysis?

The primary goal of applying regression models in data analysis is to identify relationships among different variables and to predict outcomes based on those relationships. Regression analysis allows analysts to understand how the dependent variable changes when one or more independent variables are varied while holding others constant. This capability is particularly useful in fields such as economics, medicine, and social sciences, where understanding the impact of multiple factors on a single outcome is crucial.

Regression models can quantify relationships, showing not only the direction and strength of the relationship but also how much one variable affects another. For instance, in a study examining the impact of education and experience on salary, regression can reveal how much salary is expected to increase with each additional year of education, controlling for experience. This predictive power is what makes regression a fundamental tool in data analysis, allowing for informed decision-making based on empirical data.