Multiple Regression: Key Characteristics

Multiple regression is a statistical technique used to predict the value of a dependent variable (criterion) based on the values of two or more independent variables (predictors). Let's analyze the given statements:

Statement Analysis

• (A) There can be more than two criterion: This is generally false for standard multiple regression. Multiple regression typically involves one criterion variable and multiple predictor variables. If multiple criterion variables are involved, it's termed multivariate regression.
• (B) There can be more than two predictors: This is true. The defining feature of multiple regression is the use of multiple predictor variables to explain the variance in a single criterion variable.
• (C) It indicates linear relation between one predictor and one criterion: This statement describes simple linear regression, not multiple regression. Multiple regression examines the combined effect of multiple predictors on one criterion.
• (D) The equation for regression line contains partial regression coefficients: This is true. The multiple regression equation estimates the relationship between each predictor and the criterion, controlling for the influence of other predictors. These coefficients are known as partial regression coefficients (or standardized beta coefficients).

Conclusion on Statements

Based on the analysis, statements (B) and (D) accurately describe characteristics of multiple regression.

Final Answer Determination

The correct statements are (B) and (D). Therefore, the option indicating that only (B) and (D) are correct is the right choice.