Examples of 'least-squares' in a sentence
Meaning of "least-squares"
least-squares: a statistical method used to find the best-fitting linear regression line through a set of data points. It aims to minimize the sum of the squares of the vertical deviations between the data points and the line
How to use "least-squares" in a sentence
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least-squares
One such method is least-squares approximation.
Least-squares estimation and related techniques.
Regression analysis was performed by the least-squares method.
A least-squares analysis provides the rovibrational constants.
The parameters are estimated using a generalized least-squares criterion.
The nonlinear least-squares regression curves are superimposed.
The calibration curve shall be calculated by the method of least-squares.
The linear least-squares regression lines are shown.
The solution is calculated with a least-squares solution function.
Partial least-squares analysis was used to order the traits by importance.
The line plotted is calculated by least-squares regression analysis.
The least-squares line fit to the log transformed data is shown.
It can be done conventionally by a least-squares algorithm such as the gradient algorithm.
The identification of the two potentials is done by solving a least-squares problem.
It introduces a novel least-squares linear solver that solves the issues.
See also
The operation of minimization of the functional comprises a least-squares minimization ;.
Preferably, a least-squares algorithm is used in determining the values of the estimates.
Binding data were analyzed by a nonlinear least-squares curve fitting program.
For example, the least-squares method can be viewed as a simple form of regularization.
Inhibition data were analyzed by nonlinear least-squares regression using SigmaPlot.
Constructing a least-squares selection index with this feature involves a number of questions.
The work discusses mainly maximum likelihood and least-squares methods.
The calibration curve by means of a least-squares method showed a good linearity as follows.
The total mass of the vehicle is estimated by a recursive least-squares algorithm ;.
A least-squares procedure is used to fit the property value qj to each sphere cavity j.
Inhibition data were analyzed by non-linear least-squares regression using SigmaPlol.
The least-squares line fit to the log transformed data is also plotted.
The fitting may be done by the well known least-squares fitting algorithm.
Ordinary least-squares and orthogonal distance regression is used for the parameters estimation.
Construct a calibration curve by performing a least-squares fit of equation 2 to the data.
This work proposes a phasor estimation algorithm based on a modified recursive least-squares.
For supervised learning, this includes least-squares regression and logistic regression.
Such solutions involve the minimization of square differences, called least-squares techniques.
One approach is to apply a least-squares fitting to create the appropriate fast search curve.
The curves were fit to the data by using nonlinear least-squares method.
First method consists of a least-squares solution of the equation of apparent motion over a local window.
Competition binding data were analyzed by a nonlinear least-squares curve fitting procedure.
The nonlinear least-squares regression algorithm generates different parameter combinations corresponding to different functions.
Typically, the straight line is obtained by performing a least-squares fit of the data.
A least-squares method jointly optimizes the filter gains and accounts for the band leakage.
Candidate vector is constructed from each set by employing a least-squares solution method ;.
Least-squares matrix, full H atoms treated by a mixture of independent and constrained refinement.
A formulation of this inverse problem using least-squares fit leads to a shape optimization problem.
The coef-ficients of the linear approximations are obtained from the least-squares method.
It 's a classical problem for the nonlinear least-squares method with weights and separable variables.
This controller uses a modified robust parameters identifier based on a recursive least-squares algorithm.
The algorithm applies a deterministic time-domain least-squares criterion within each of a series of data blocks.
These estimates are necessary because the calculation uses a non-linear least-squares algorithm.
The values of IC i ini are adjusted by a least-squares type method by integrating chemical constraints.
Such a problem can then be solved notably by using a non-linear least-squares routine.
