# least squares

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## least squares

pl.n. Statistics
A method of determining the curve that best describes the relationship between expected and observed sets of data by minimizing the sums of the squares of deviation between observed and expected values.

## least squares

n
(Mathematics) a method for determining the best value of an unknown quantity relating one or more sets of observations or measurements, esp to find a curve that best fits a set of data. It states that the sum of the squares of the deviations of the experimentally determined value from its optimum value should be a minimum

## least′ squares′

n.
a statistical method of estimating values from a set of observations by minimizing the sum of the squares of the differences between the observations and the values to be found.
Also called least′-squares′ meth`od.
[1860–65]
ThesaurusAntonymsRelated WordsSynonymsLegend:
 Noun 1 least squares - a method of fitting a curve to data points so as to minimize the sum of the squares of the distances of the points from the curvemethod of least squaresstatistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parametersstatistical method, statistical procedure - a method of analyzing or representing statistical data; a procedure for calculating a statistic
References in periodicals archive ?
1) by a penalized least-squares problem of the form
Since there is no zero-component solution of the least-squares problem, the solution from each major iteration improves strictly so that there is no degeneracy.
Next, we first transform the least-squares problem with respect to the matrix equation (4.
If m is equal to n, the package solves the nonlinear equations problem, F(x) = 0, while if m is greater than n, it solves the nonlinear least-squares problem, [Mathematical Expression Omitted].
In order to minimize round-off errors, we avoided using explicit inverse methods to solve this least-squares problem.
YUAN, Linearized alternating method for constrained least-squares problem, East Asian J.
This problem, which we call the linearized least-squares problem for rational interpolation, is the starting point of the algorithm we recommend in this article, and we describe the mathematical basis of how we solve it in the next section.
Hough and Vavasis in [17] developed an algorithm to solve an ill-conditioned full rank weighted least-squares problem using RRQR factorization as a part of their algorithm.
Lebret, "Robust solutions to least-squares problems with uncertain data," SIAM Journal on Matrix Analysis and Applications, vol.
The material is geared toward scientists and engineers who analyze and solve least-squares problems, but is also suitable for a graduate or advanced undergraduate course for students with a working knowledge of linear algebra and basic statistics.
He proceeds by examining vector spaces and linear transformations, explores the Moore-Penrose pseudouniverse, introduces singular value decomposition, describes linear equations, projections, inner product spaces, norms, linear least-squares problems, eigenvalues and eigenvectors.

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