To accurately predict the dependent variable in simple linear regression, what is needed?

In simple linear regression, a linear function that accurately models the relationship between the independent variable and the dependent variable is essential for making predictions. This is because simple linear regression aims to figure out how the independent variable influences the dependent variable through a straight-line equation, often represented as ( Y = mX + b ), where ( Y ) is the predicted value of the dependent variable, ( m ) represents the slope of the line, ( X ) is the independent variable, and ( b ) is the y-intercept.

The correct answer focuses on the need for a linear relationship, which is fundamental to linear regression analysis. This linear function provides the framework for how changes in the independent variable will lead to predictable changes in the dependent variable.

Other options, such as having multiple independent variables, only the independent variable data, or relying on a complex interaction model, do not align with the principles of simple linear regression. Simple linear regression specifically uses one independent variable to predict a dependent variable, thus simplifying the modeling process.