Least Squares Estimation is a foundational inferential technique within regression analysis that identifies model parameters by minimizing the sum of squared residuals between observed data points and predicted values. The method relies on calculus-based optimization to derive closed-form solutions under conditions including linearity, independence, homoscedasticity, and normal distribution of errors. It serves as the theoretical bedrock for Ordinary Least Squares (OLS) models, establishing unbiased estimators with minimum variance according to the Gauss-Markov theorem in linear statistical modeling domains.
Prerequisite chain context: requires Scatter Plot Visualization Techniques in Data Analysis.