The method of least squares, developed by Steven J. Miller, is a fundamental statistical technique used to find the best-fitting line or curve to a set of data points. It minimizes the sum of the squared differences between the observed data and the fitted curve, providing an optimal approximation for data analysis and modeling. This method underlies various statistical methods, including linear regression, hypothesis testing, and curve fitting, and has wide applications in fields like data fitting, statistical modeling, and engineering.
