Which method extends simple linear regression by considering several causes at once to explain the outcome?

Which method extends simple linear regression by considering several causes at once to explain the outcome?

• A. Simple Linear Regression
• B. Multiple Linear Regression
• C. Intercept
• D. Dummy Variables

Answer: (B)

Multiple linear regression. It extends simple linear regression by including several predictors in the model, allowing you to explain the outcome with multiple causes at once. The typical form is y = β0 + β1x1 + β2x2 + … + βk xk + ε, so you can estimate the effect of each factor while controlling for the others, often improving explanatory power and prediction. The intercept is simply the constant term in the model, not the method itself, simple linear regression uses only one predictor, and dummy variables are a way to include categorical predictors—not the overall approach.

Multiple linear regression. It extends simple linear regression by including several predictors in the model, allowing you to explain the outcome with multiple causes at once. The typical form is y = β0 + β1x1 + β2x2 + … + βk xk + ε, so you can estimate the effect of each factor while controlling for the others, often improving explanatory power and prediction. The intercept is simply the constant term in the model, not the method itself, simple linear regression uses only one predictor, and dummy variables are a way to include categorical predictors—not the overall approach.