Sentence examples for class sum from inspiring English sources

There are no underlying distributional assumptions associated with this technique, it is entirely data-driven, but it is possible to make an ad-hoc assessment of the goodness of fit by measuring the within class sum of square differences from class means.

Table 3 Factorial ANOVA for the five largest individuals within each coverage-distance class   Sum of squares Degrees of freedom Mean squares F p value Vegetation type 573.4 2 286.7 56.89 8E−10 Distance to propagule source 213.3 1 213.3 42.34 1E−06 Interaction 443.6 2 221.8 44.01 9E−09 The group has five individuals with the highest diameters at breast height.

However, once we standardize the within class sum of squares, the SWISS scores have the same scale and are comparable.

Using the within class sum of squares to compare how well data are clustered has appeared before in the literature.

Additionally, Calinski and Harabasz [14] proposed a method based on within and between class sum of squares that was repeatedly shown to perform well for choosing k.

As previously mentioned, because we are standardizing the within class sum of squares by dividing by total sum of squares (giving a value between zero and one), SWISS can be used to compare methods that are on different scales.

And then there was Lennox Lewis, whose victory in the newly introduced super-heavyweight class summed up all that was wrong with those Games.

After several cycles of multi-reference alignment, multivariate statistical analysis and classification, final class sums from all analyzed particles were obtained (Figure 3A).

Initial reconstructions were calculated by back-projection of either of class sums or aligned raw images, up to 35° from the side view plane, and symmetry estimated by maximising density variance within the maps.

Statistical differences in the observed mutational specificities among the microsatellite alleles were analyzed using the χ or Fisher exact test and the numbers of mutants in each class (summed for both strands).

To investigate this variance ratio, we use the Calinski-Harabasz Index (CH) [ 24], which is defined as: CH = S S B / k − 1 S S W / n − k where SS B is the overall between-class sum of squared deviations, SS W is the overall within-class sum of squared deviations, k is the number of classes, and n is the number of observations.