Least squares
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In regression analysis, least squares is a method to determine the best-fit model by minimizing the sum of the squared residuals—the differences between observed values and the values predicted by the
Carl Friedrich Gauss
Ordinary least squares
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In statistics, ordinary least squares is a type of linear least squares method for choosing the unknown parameters in a linear regression model by the principle of least squares: minimizing the sum of
Least squares
…residuals—the differences between observed values and the values predicted by the model. Least squares problems fall into two categories: linear or ordinary least squares and nonlinear least squares, depending on whether or not the model functions are linear in all unknowns. The linear least-squares problem occurs in…
Residuals to a quadratic fit for correctly and incorrectly converted data.