# least squares

(redirected from Sum of squared error)
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## Synonyms for least squares

### a method of fitting a curve to data points so as to minimize the sum of the squares of the distances of the points from the curve

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References in periodicals archive ?
(8) if the sum of squared errors has not decreased, discard the new weights, increase [lambda] using 5, and go to step 4;
In other words, after establishing a linear equation, y = ax + b, where variables x and y have dependent relationship with each other and regression processing derives a and b that make the sum of squared errors minimum among given data sets.
Instead of minimizing the sum of squared errors (SSE) in LSR, it minimizes the sum of absolute errors (SAE).
The Ward Method of Minimum Variance is biased to form groups of the same size, from the sum of squared errors (SSE) of each cluster (sum of squared deviations for each centroid of the cluster).
The weight is chosen to minimize the sum of squared errors for the new estimate.
In the first run, all starting values equal one, and the fit-function returns a point in the parametric space (i.e., values of 10 parameters) and a corresponding sum of squared errors. Let us denote this point as an acceptable point.
Mean and Sum of Squared Errors of the Current Month's Recession Probability Estimates Mean Squared Error Aggregate Economic Labor (%) Sentiment (%) activity (%) (%) All months 1.8 3.8 2.0 3.0 First four months of 17.8 27.6 17.2 22.4 recessions Rest of recessions 0.2 11.1 1.9 5.4 Expansions 0.9 1.4 1.0 1.5 Sum of Squared Errors Aggregate Economic activity Labor Sentiment All months 6.5 13.9 7.4 11.1 First four months of 3.6 5.5 3.4 4.5 recessions Rest of recessions 0.1 4.0 0.7 2.0 Expansions 2.9 4.4 3.2 4.7
Performance index used in this paper is the sum of squared errors,
The sum of squared errors is minimized by equating the derivative of the squared errors to zero.
(4) Convergence is monitored by observing sum of squared errors of the observed values and corresponding predicted values from step (2).
is the sum of squared errors in the low group, and [Y.sub.ij] is the [j.sup.th] individual phenotype in the [i.sup.th] cell.
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