Which statement best describes Simple Regression within Cause and Effect models?

Which statement best describes Simple Regression within Cause and Effect models?

• A. It models relationship with one predictor variable.
• B. It uses multiple predictor variables
• C. It is a time series model
• D. It is the same as naive forecasting

Answer: (A)

Simple Regression analyzes how a single predictor influences the outcome. It fits a straight-line relationship between one cause (the predictor) and the effect, typically written as y = β0 + β1x, where β1 represents how much the outcome changes per unit change in the predictor. This focus on one predictor is what defines simple regression. It’s not built for multiple predictors—that would be multiple regression. It’s also not specifically a time series model, although you could apply regression to time-based data; the defining feature remains the single predictor. And it isn’t the same as naive forecasting, which uses simple rules like “last value,” rather than estimating a relationship between variables. So the best description is that simple regression models the relationship with one predictor variable.

Simple Regression analyzes how a single predictor influences the outcome. It fits a straight-line relationship between one cause (the predictor) and the effect, typically written as y = β0 + β1x, where β1 represents how much the outcome changes per unit change in the predictor. This focus on one predictor is what defines simple regression.

It’s not built for multiple predictors—that would be multiple regression. It’s also not specifically a time series model, although you could apply regression to time-based data; the defining feature remains the single predictor. And it isn’t the same as naive forecasting, which uses simple rules like “last value,” rather than estimating a relationship between variables.

So the best description is that simple regression models the relationship with one predictor variable.