What is regression analysis used to understand?

Regression analysis is a powerful statistical method primarily used to understand the relationship between a dependent variable and one or more independent variables. This technique allows analysts to model and analyze the factors that influence a particular outcome, making it invaluable for predictive analytics and decision-making.

By establishing a relationship through regression, one can quantify how changes in the independent variable(s) affect the dependent variable. For instance, in a simple linear regression scenario, you might assess how the number of hours studied (independent variable) impacts exam scores (dependent variable). The outputs, often in the form of coefficients, can indicate the strength and direction of these relationships.

While correlation between two categorical variables, trend analysis over time, and data classification into groups are important aspects of data analysis, they are not the focus of regression analysis. Each of those areas relies on different statistical methods tailored to their specific types of data and analytical goals. Thus, recognizing the role of regression helps in applying it effectively to real-world situations where understanding causation and prediction are critical.