least squares

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least squares

(lēst skwārz),
A principle of estimation invented by Gauss in which the estimates of a set of parameters in a statistical model are the quantities that minimize the sum of squared differences between the observed values of the dependent variable and the values predicted by the model.

least squares

a method of regression analysis. The line on a graph that best summarizes the relationship between two variables is the one that ensures that there is the least value of the sum of the squares of the deviation between the fitted curve and each of the original data points.
References in periodicals archive ?
The basic problem used in this paper is the non-negative least-squares (NNLS) problem, minimizing [[parallel]Ex - b[parallel].
In the presence of such heterogeneity, conventional least-squares regression models may underestimate, overestimate, or fail to detect important changes occurring locally at a certain quanfile of data, because it focuses on changes in the means (Terrell et al.
Borin A, Ferrao MF, Mello C, Maretto DA, Poppi RJ (2006) Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk.
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.
Recent work on Shepard methods have focused on reducing the computational cost by limiting the least-squares fitting process to a local subset of the data.
Next, we first transform the least-squares problem with respect to the matrix equation (4.
Almost half a century ago, Henri Theil introduced symmetric price and quantity index numbers based on an elegant least-squares principle.
The uncertainties identified by item (a) contribute to the deviations in the responses from the least-squares fit to the data and are accounted for by the uncertainty [u.
When the model is correct and the observations are affected only by random errors then the classic least-squares estimation yields the most likely solution.
Regression "instruments" and the regression procedure known as two-stage least-squares are typically used to estimate a demand equation.

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