# 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 ?
However, enough knowledge and experience have already been gained to be able to say that currently proposed alternative methods give sound results, have worthwhile advantages over the least-squares methods and can be recommended for practical use.
In Section 4 a system of linear inequalities is solved using the least-squares method.
Because ArcView GIS relies on either the weighted least-squares method or visual adjustment to create semivariograms, we did not compare the relative performance of semivariograms generated using alternative methods such as maximum likelihood and restricted maximum likelihood.
The least-squares method is used to adjust the Kelvin model by fitting it to creep data from experiments.
The values obtained in comparison with the modified ALBK method were fitted to a linear regression model using a least-squares method.
This was done by a nonlinear least-squares method (25): denoting the experimental data for the storage and the loss modulus by [Mathematical Expression Omitted] and [Mathematical Expression Omitted] and assuming the data to be affected by relative errors of unknown size
The worst case is that for the least-squares method and an analytical CV of 2% for the x measurements (bias, -35%).
We suspect that these CO-oximeters with overdetermined systems use the least-squares method for data reduction.
Other complex pharmacokinetic methods also exist that require dedicated software and use either non-Bayesian least-squares (where the population model is not well-known) or Bayesian least-squares methods (where the population model is reasonably well-known) (2).
The text consist of 11 parts: optimal estimation, linear estimation, stochastic gradient algorithms, mean-square performance, transient performance, block adaptive filters, least-squares methods, array algorithms, fast RLS algorithms, lattice filters, and robust filters.
Surfaces Generated by Moving Least-Squares Methods, Mathematics of Computation, Vol.

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