Which statement describes the extension of simple regression to predict an outcome by a linear combination of two or more predictors?

Which statement describes the extension of simple regression to predict an outcome by a linear combination of two or more predictors?

• A. Multivariate
• B. Moses extreme reactions
• C. Multicollinearity
• D. Multiple regression

Answer: (D)

Extending simple regression to predict an outcome using more than one predictor is described by multiple regression. This approach uses a linear equation that combines several predictors, for example Y = β0 + β1X1 + β2X2 + ... + ε, estimating how each predictor contributes to the outcome while holding the others constant. The idea is still about a linear relationship, but now the prediction is based on several factors rather than just one. Multivariate relates to multiple dependent variables, multicollinearity refers to high correlations among the predictors themselves, and the unrelated distractor is not a statistical term.

Extending simple regression to predict an outcome using more than one predictor is described by multiple regression. This approach uses a linear equation that combines several predictors, for example Y = β0 + β1X1 + β2X2 + ... + ε, estimating how each predictor contributes to the outcome while holding the others constant. The idea is still about a linear relationship, but now the prediction is based on several factors rather than just one. Multivariate relates to multiple dependent variables, multicollinearity refers to high correlations among the predictors themselves, and the unrelated distractor is not a statistical term.