In regression, predictor variables are commonly called:

In regression, predictor variables are commonly called:

• A. Independent variables
• B. Dependent variables
• C. Moderator variables
• D. Confounding variables

Answer: (A)

In regression, the variables you use to predict or explain the outcome are called independent variables. They serve as the inputs to the model; their values are treated as given or fixed when estimating how they relate to the outcome. The outcome you’re trying to predict is the dependent variable. This naming reflects their role as explanatory inputs, in contrast to the dependent variable, which is the result you’re modeling. Terms like moderator or confounding variables refer to specific roles a variable can play in relation to the predictors and outcome, not to the basic label of the predictors themselves. A moderator changes the strength or direction of the predictor’s effect, often via an interaction term, while a confounder is a variable that biases the association by being related to both predictor and outcome.

In regression, the variables you use to predict or explain the outcome are called independent variables. They serve as the inputs to the model; their values are treated as given or fixed when estimating how they relate to the outcome. The outcome you’re trying to predict is the dependent variable.

This naming reflects their role as explanatory inputs, in contrast to the dependent variable, which is the result you’re modeling. Terms like moderator or confounding variables refer to specific roles a variable can play in relation to the predictors and outcome, not to the basic label of the predictors themselves. A moderator changes the strength or direction of the predictor’s effect, often via an interaction term, while a confounder is a variable that biases the association by being related to both predictor and outcome.