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 addition of a least-squares fitting condition again requires extra flexibility, which we get by using a quadratic spline with one knot.
When using linear least-squares fitting, weighting factors are determined from the data matrix and the matrix of pure components.
The ability to improve the interpretation of the data by using linear least-squares fitting increases the value of these experiments even more.
Linear least-squares fitting results in less background noise from each of the elements within the layers of a profile, which means an increase in detection limits.
The method utilizes an iterative non-linear least-squares fitting process starting from an initial estimated solution.
With the available least-squares fitting program used, the parameter Cg must be empirically determined and used as a constant in the fit, all other parameters being kept as variables.
Statham, Deconvolution and Background Subtraction by Least-Squares Fitting with Prefiltering of Spectra, Anal.

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