error sum of squares

error sum of squares

[¦er·ər ¦səm əv ¦skwerz]
(statistics)
In analysis of variance, the sum of squares of the estimates of the contribution from the stochastic component. Also known as residual sum of squares.
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One is for the group sum of squares in the numerator and one is for the error sum of squares in the denominator.
Total Sum of Squares (TSS) = Error Sum of Squares (ESS) + Regression Sum of Squares (RSS) (15)
2]), mean square error (MSE), root mean square error (RMSE), error sum of squares (SSE) and prediction sum of squares (PRESS) were calculated.
2]), mean square error (MSE), error sum of squares (SSE) and predicted residual error sum of squares (PRESS) as described in Ghoreishi et al.
To identify an optimal grouping of participants in the clustering hierarchy, the agglomeration schedule was examined to find a late stage in the hierarchy, with a relatively small number of participant clusters, where in which the error sum of squares coefficients increased dramatically at subsequent stages in the hierarchy, after relatively small increases at previous stages (Berven & Hubert, 1977).
be a measure of efficiency of an experimental design, where SST stands for the total sum of squares, SSE stands for the error sum of squares.
Thus, while the adjusted error sum of squares (SS[E.
13614832 Parameter Standard Variable Estimate Error Sum of Squares F Prob>F INTERCEP 1.
Since we are dealing with several populations, we use the sample data to measure three sources of variability: treatment variation or treatment sum of squares, which measures how sample results differ among the various treatments; error variability or error sum of squares, which measures collectively how the observations vary within their respective samples; and total variability or sum of squares, which measures total variation of the sample observations for the whole experiment without regard to the number of populations.
E]) are reduced without a comparable reduction in error sum of squares (S[S.
Next, we obtain the error sum of squares by subtraction:
The error sum of squares of the whole period (ESS1) was compared with the sum of the error sum of squares of the two subperiods (ESS2).