Which statement describes the two models of Cause and Effect?

Which statement describes the two models of Cause and Effect?

• A. Time Series
• B. Simple regression and multiple regression
• C. Naive forecasting
• D. MAD and MAPE

Answer: (B)

Cause-and-effect forecasting relies on identifying factors that drive the outcome and quantifying their impact. The two models that express that relationship are simple regression, which uses one predictor, and multiple regression, which uses several predictors. In simple regression, you predict the dependent variable from a single driver, capturing the linear relationship with that one variable. In multiple regression, you incorporate several drivers, modeling the dependent variable as a combination of multiple influences. Time series forecasts look at patterns in past data rather than explicit causal drivers, Naive forecasting uses just the last observed value, and MAD or MAPE are measures of forecast accuracy, not models.

Cause-and-effect forecasting relies on identifying factors that drive the outcome and quantifying their impact. The two models that express that relationship are simple regression, which uses one predictor, and multiple regression, which uses several predictors. In simple regression, you predict the dependent variable from a single driver, capturing the linear relationship with that one variable. In multiple regression, you incorporate several drivers, modeling the dependent variable as a combination of multiple influences. Time series forecasts look at patterns in past data rather than explicit causal drivers, Naive forecasting uses just the last observed value, and MAD or MAPE are measures of forecast accuracy, not models.