The least squares method is a statistical technique used to minimize the difference between observed data and a mathematical model, typically by finding the best-fitting curve or line. This approach is widely applied in regression analysis, where it helps determine the coefficients of a model that best approximate a set of data points. By minimizing the sum of the squares of the residuals (the differences between observed and predicted values), this method aids in finding the most accurate representation of data, connecting seamlessly with rational approximations and Hilbert space frameworks.
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