Which method draws the best straight line to explain how one variable changes when another single variable changes?

Which method draws the best straight line to explain how one variable changes when another single variable changes?

• A. Simple Linear Regression
• B. Multiple Linear Regression
• C. Functional Forms
• D. Non-Linear Terms

Answer: (A)

Fitting a straight-line relationship between two variables is best done with simple linear regression. It finds the line that describes how the dependent variable changes as the independent variable changes, using all observed data points. The line is chosen by the least squares criterion, meaning it minimizes the sum of the squared vertical distances between the observed values and the line. The slope tells you how much the dependent variable changes with a one-unit change in the predictor, while the intercept gives the predicted value when the predictor is zero (its practical meaning depends on whether zero is a realistic predictor value). This approach is ideal when you expect a linear relationship with one predictor. If there are multiple predictors, or if the relationship is not linear, other methods are more appropriate, such as multiple linear regression for several predictors, or functional forms/nonlinear terms for curved relationships.

Fitting a straight-line relationship between two variables is best done with simple linear regression. It finds the line that describes how the dependent variable changes as the independent variable changes, using all observed data points. The line is chosen by the least squares criterion, meaning it minimizes the sum of the squared vertical distances between the observed values and the line. The slope tells you how much the dependent variable changes with a one-unit change in the predictor, while the intercept gives the predicted value when the predictor is zero (its practical meaning depends on whether zero is a realistic predictor value). This approach is ideal when you expect a linear relationship with one predictor. If there are multiple predictors, or if the relationship is not linear, other methods are more appropriate, such as multiple linear regression for several predictors, or functional forms/nonlinear terms for curved relationships.