Least Squares
Least squares is a mathematical optimization method used to find the best-fitting curve or line to a set of data points by minimizing the sum of the squares of the differences (residuals) between observed and predicted values. It is widely applied in regression analysis, statistics, and machine learning to estimate parameters in models. The method provides a way to solve overdetermined systems of equations where there are more equations than unknowns.
Developers should learn least squares when working on data analysis, predictive modeling, or machine learning projects, as it is fundamental for linear regression, curve fitting, and parameter estimation. It is essential in fields like econometrics, engineering, and data science for tasks such as trend analysis, forecasting, and optimizing model accuracy by reducing error. Understanding least squares helps in implementing algorithms for gradient descent, regularization techniques, and statistical inference.