Sentence examples for variance class from inspiring English sources

The use of classes of environments to account for the heterogeneous residual variance (CLASS) slightly reduced the bias of the genetic correlation between slope and intercept, but had little impact on the other genetic variance components.

All parameters were estimated (including proportion of SNPs in each variance class) as described in Erbe et al. [ 12] and Kemper et al. [ 41] with BayesR.

This model was characterized by strongest preferences for the following: Attachment (n = 216) Stability, with autonomy being a close second (n = 197) There appear to be very small variances on the latent scale, at least for those 351 respondents in the high scale (low variance) class.

About 98% and 90% of the MAPs were higher than 0.85 regarding, respectively, the average adherence latent trajectories and the standardized variance in adherence latent trajectories.> -wrap-foot> Bold numbers are the numbers of patients in the nine average-standardized variance classes of adherence.

The discriminativeness of each feature component is closely related to the well-known Fisher's linear discriminant criterion [27], where the discriminant criterion is defined to be the ratio of between-class variance inter-class variance inter-class-class variation(intoa-class variation).

For each learning environment feature (at the bottom of the table), ICC 2 (= between-class variance/(between-class variance + (within-class variance/mean class size))) was calculated as a measure of reliability at the class level: comp 0.81, auto 0.86, relat 0.33, struc 0.79, tol.

Heterogeneity of some of the classes and the residual noise (variance within class) which remain even after discarding the fourth and the subsequent PCs: as mentioned previously, variance within class in real-life datasets, unlike the variance within class in toy datasets (which is fixed at 1), differs from class to class even within the same dataset.

Variance within class in real-life datasets, unlike variance within class in toy datasets (which is fixed at 1 in this study), differs from class to class even within the same dataset, and is likely to be larger than 1.

Jenk's natural breaks classification was developed to identify the ideal arrangement of values (e.g. rates) into different classes, by reducing the variance within classes and maximizing the variance between classes [ 51].

For such enrollment levels variance of class size is mainly exogenous and we argue that this allows estimation of quasi-experimental class size effects.

Hence, there is no guarantee that the directions of maximum variance enhance class separabilities.