Linear least squares (LLS) is the least squares approximation of linear functions to data.It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted),weighted, and generalized (correlated) residuals.Numerical methods for linear least squares include inverting the matrix of the normal equations and orthogonal decomposition methods.The OLS method minimizes the sum of squared residuals, and leads to a closed-form expression for the estimated value of the unknown parameter vector β: Linear least squares (LLS) is the least squares approximation of linear functions to data.It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted),weighted, and generalized (correlated) residuals.Numerical methods for linear least squares include inverting the matrix of the normal equations and orthogonal decomposition methods.