Select the general form of a linear regression equation.

The general form of a linear regression equation is represented as Y = b0 + b1(X). In this equation, Y is the dependent variable that we are trying to predict or explain, X is the independent variable or predictor, b0 is the y-intercept (the value of Y when X equals zero), and b1 is the slope of the line, which represents the change in Y for a one-unit change in X.

This form effectively captures the linear relationship between the variables, which is the essence of linear regression. It provides a clear and straightforward model to understand how changes in the independent variable affect the dependent variable without introducing any complexities like exponents or shifts, which could lead to non-linear interpretations.

Understanding this form is critical in QDM as it underlines the importance of modeling relationships between variables to inform decision-making and quality improvements based on data analysis.