What is regression analysis used for?

Regression analysis is a statistical technique primarily employed to model and analyze the relationships between dependent and independent variables. It helps in understanding how the value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. This modeling process is fundamental in predicting outcomes and making informed decisions based on the relationships derived from historical data.

By using regression, researchers and analysts can quantify the strength and form of these relationships, allowing them to explore causality, make forecasts, and assess how different factors contribute to an outcome. This method is widely applied across various fields, including economics, biology, engineering, and social sciences, to infer patterns and trends based on empirical evidence.

While the other choices might relate to data analysis, they do not capture the specific utility of regression analysis. Grouping similar data points pertains more to clustering techniques, identifying the accuracy of data relates to validation methods rather than regression itself, and visualizing data trends generally involves techniques like graphing and plotting instead of directly modeling relationships between variables. Therefore, the essence of regression analysis lies firmly in modeling the relationship between variables.