residual sum of squares


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residual sum of squares

[rə¦zij·ə·wəl ¦səm əv ′skwerz]
(statistics)
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In all cases the polynomial model is preferred as the coefficient of determination R2 is higher, the residual sum of squares for polynomial regression is smaller than the residual sum of squares for linear regression and DAIC has a negative value.
It can be seen that in the extraction of two components, the RMS equation predicted residual sum of squares.
Selection of the best model describing the age-length relationship was performed using both the residual sum of squares (RSS) and inspection of the L[?
Residual sum of squares of the optimal in-season model was lower than that of the pre-season forecast (sibling) model, indicating the former was more accurate than the latter.
2]), standard deviation of estimate (SE), residual sum of squares (RSS) and the tendency of residual distribution (Goneli et al.
Where SSR and SST are the residual sum of squares and total sum of squares respectively.
Several possible starting values were selected for each parameter and the starting value which has the smallest residual sum of squares was accepted.
The value of optimized parameters, residual sum of squares (SSQ), and regression between the observed and predicted values (R2) are listed in these Tables.
A model with a large regression sum of squares in comparison to the residual sum of squares indicates that the model accounts for most of variation in the dependent variable.
R] = the estimated residual sum of squares (RSS) of the full (F) and reduced (R) models, respectively (Kimura, 1980; Quinn and Deriso, 1999).
Model selection : Model comparison and selection was based on statistics of predicted residual sum of squares (PRESS) because prediction is the most important focus here.
That is, the "V" trend is expected to be the most frequent since that would imply that residual sum of squares is a minimum when correlation of the error term is smallest (negative or positive).