Simple Linear Regression models a stochastic relationship between a dependent variable and a single continuous independent variable using a linear equation with parameters estimated via the method of least squares. This statistical framework minimizes the sum of squared residuals to determine the line that best fits observed data points within the domain of predictive analytics. It serves as the fundamental subfield for univariate analysis in regression theory, establishing the baseline against which multivariable extensions are defined and evaluated.
Prerequisite chain context: requires Least Squares Estimation Method in Regression Modeling.