What statistical method analyzes the impact of multiple independent variables on a single outcome variable?

Multiple regression is a statistical method specifically designed to analyze the relationship between multiple independent variables and a single dependent (outcome) variable. This approach allows researchers to assess how each independent variable contributes to the variation in the dependent variable while controlling for the effects of the other independent variables in the model.

By using multiple regression, researchers can identify the strength and nature of the relationships among the variables, making it a powerful tool in understanding complex interactions within data. The coefficients generated in a multiple regression analysis indicate the direction and magnitude of the relationship between each independent variable and the dependent variable, providing insights into how changes in the independent variables can influence the outcome.

In contrast, simple regression involves only one independent variable predicting a dependent variable, while linear regression refers to any regression model that assumes a linear relationship without specifically emphasizing the number of independent variables. ANOVA, on the other hand, is used to compare means across different groups rather than assessing individual contributions of multiple predictors to a single outcome variable. This distinction highlights why multiple regression is the appropriate choice for analyzing the impact of several independent variables.