In a multiple linear regression equation, which variable is typically manipulated to see its effect on the dependent variable?

In multiple linear regression, the independent variable is the one that researchers manipulate or observe to assess its impact on the dependent variable. The primary goal of a regression analysis is to understand how changes in these independent variables influence the outcome represented by the dependent variable.

By adjusting the values of the independent variables, analysts can observe variations in the dependent variable, enabling them to infer relationships and make predictions. This manipulation of independent variables helps identify causal patterns, determine the strength and direction of relationships, and ultimately aid in decision-making processes in various fields such as economics, healthcare, and social sciences.

In this context, the dependent variable is the outcome that we are trying to predict or explain, while the control variable typically refers to factors held constant to isolate the effects of the independent variables on the dependent variable. The term "predicting variable" can sometimes be synonymous with independent variable, but in the context of typical regression nomenclature, independent variable is the more accurate and widely accepted term.