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.
References in periodicals archive ?
An alternative bias estimation technique and an application of the least squares technique in multiple linear connections.
If [P.sub.m] and [P.sub.m+1] are [H.sub.m.sup.T] [Q.sub.m.sup.-1] [H.sub.m] and [H.sub.m+1.sup.T] [Q.sub.m+1.sup.-1] [H.sub.m+1.sup.T] separately, the recursive least squares estimation for the load inertia can be expressed in (9) when the number m+1 set of test data is observed.
These are the required least squares estimates of a and b that minimise E in equation (1).
The least squares solution with least norm is [x.sub.0] = [N.sup.[dagger]]c.
To improve the convergence speed, we derive a least squares based iterative (LSI) identification algorithm.
Furthermore, we also defined a central window on k-space, in which missing data points are still interpolated by weights generated from the conventional least squares technique.
The coefficient of the variable X1 is negative, which is inconsistent with the actual situation, in order to eliminate the multicollinearity among variables, using partial least squares regression method.
This task can be accomplished by least squares method using the extended observation equation
The Beavers-Joseph-Saffman interface conditions are treated as an extra least squares functional, while boundary conditions are imposed into solution spaces.
We generally used the method of least square to estimate the parameter of linear models.
This was supported by a difference in least squares means of 0.062 kg (P less than 0.0001) in favor of males for this trait.
Hu, "Iterative and recursive least squares estimation algorithms for moving average systems," Simulation Modelling Practice and Theory, vol.

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