What defines linear regression in statistical analysis?

Linear regression is specifically characterized by its ability to model the dependence of a dependent variable on one or more independent variables. In this context, the focus is on establishing a relationship whereby changes in the independent variables are linked to changes in the dependent variable. This modeling is foundational in statistical analysis because it helps in understanding how one variable is affected by others, allowing for predictions and interpretations based on data.

While it is true that linear regression may involve multiple variables, that aspect falls under a broader category and does not exclusively define linear regression itself. Additionally, predicting future values is a subsequent benefit derived from the relationship described by the regression model rather than a defining characteristic of the regression process. Evaluating correlations, particularly in terms of time series, is a different analytical approach that does not specifically focus on the modeling aspect of linear regression, which aims more at establishing dependence rather than just correlation.