What is regression analysis primarily used for?

Regression analysis is primarily used to predict the value of a dependent variable based on one or more independent variables. This statistical technique helps analysts understand the relationships between variables, allowing them to make informed predictions about outcomes based on input data.

In regression analysis, the dependent variable is the outcome that the analysis aims to predict, while the independent variables are the factors that might influence that outcome. For example, one might use regression analysis to predict a person's weight (dependent variable) based on their height, age, and physical activity level (independent variables). This predictive capability makes regression an essential tool in various fields such as economics, biology, engineering, and social sciences, where understanding relationships among variables is crucial in decision-making processes.

The other options reflect different aspects of data analysis but do not represent the primary purpose of regression analysis. Classifying data into distinct categories relates more closely to classification algorithms rather than regression, summarizing data distributions refers to descriptive statistics that give insights into data characteristics without predicting outcomes, and visualizing data trends typically involves graphical representations like charts and plots rather than the modeling and predictive focus of regression.