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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.
The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. In regression analysis, least squares is a parameter estimation method based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each ...
National Physical Laboratory (1961), "Chapter 2: Linear Equations and Matrices: Direct Methods on Automatic Computers", Modern Computing Methods, Notes on Applied Science, vol. 16 (2nd ed.), Her Majesty's Stationery Office
The power series method calls for the construction of a power series solution = =. If a 2 is zero for some z, then the Frobenius method, a variation on this method, is suited to deal with so called "singular points". The method works analogously for higher order equations as well as for systems.
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...
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Krakiwsky, "A synthesis of recent advances in the method of least squares", Lecture Notes #42, Department of Geodesy and Geomatics Engineering, University of New Brunswick, 1975 Cross, P.A. "Advanced least squares applied to position-fixing" , University of East London , School of Surveying, Working Paper No. 6, ISSN 0260-9142 , January 1994.
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