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 modeling describes a dependent variable as a function of one or more drivers, so you can see how changes in those predictors cause changes in the outcome. Simple regression uses a single predictor to estimate that relationship, while multiple regression uses several predictors, together quantifying how each one influences the dependent variable. This pair directly represents how causes (predictors) affect the effect (outcome), which is why they describe the two models of Cause and Effect.

Time series focuses on patterns over time rather than explicit causal drivers. Naive forecasting relies on a simple rule like using the last observation, with no formal cause-and-effect relationship. MAD and MAPE are measures of forecast accuracy, not models of how predictors influence the outcome.