A statistical model that relies on a set of predictors to understand a dependent variable's behavior is called?

The focus of this question is on identifying a statistical model that utilizes multiple predictors to analyze and understand the behavior of a dependent variable. Multiple regression is specifically designed to handle situations where there are two or more independent variables (predictors) that aim to explain variations in a dependent variable. This approach allows for assessing the impact of each predictor while controlling for the effects of others, thus providing a clearer understanding of their individual contributions to the dependent variable.

In contrast, linear regression typically involves only one independent variable and examines the relationship between that single predictor and the dependent variable. While it can also be used to explore relationships, it does not accommodate multiple predictors in a single model.

Forecasting models, while related to prediction, generally emphasize future values and trends rather than focusing on the relationship between predictors and a dependent variable in the way a multiple regression does.

Time-series analysis, on the other hand, specifically relates to data collected over time and often analyzes patterns such as trends and seasonality rather than focusing on multiple predictors simultaneously.

Therefore, multiple regression is the most appropriate answer, as it clearly represents the use of multiple predictors in understanding the behavior of a dependent variable.