Sentence examples for combination of variance from inspiring English sources

Analysis of covariance (ANCOVA), which can be defined as the combination of variance analysis and regression analysis, is a powerful technique that can be applied when assumptions are met.

The standard errors are based on a combination of variance components rather than a least squares estimate of variance so that they are not associated with a known number of degrees of freedom (df).

As noted from the numerical examples (table 2), statistical performance seems to be considerably more sensitive to non-optimal strategies at some combinations of variance components, unit costs and cost function exponents than at others.

At other combinations of variance components and unit costs, in particular when between-subject variability was small compared to within-subject variability and subject recruitment costs at the same time were large compared to costs for setting up measurement occasions, non-linearities did, however, strongly affect both the optimal allocation principle and the eventual statistical performance.

The same self-reliance rate reduction could also be justified by a 0.28 increase in the log of the coefficient of absolute risk aversion, or some combination of added variance and added risk aversion.

The new decision rule referred as soft-majority voting (S-MV) forces the classification as a combination of minimum variance with the highest posterior probabilities for each category thus strengthens the majority voting.

This increase resulted from a combination of increasing variance and decreasing mean rate.

We ran 500 simulations for each combination of residual variance, correlation, model, and sample size.

Intra-group variance is a combination of biological variability and technical-derived variance associated with RNA preparation, cDNA production and LRE qPCR analysis, whereas inter-group variance is primarily reflective of biological variance.

MSCA is a combination of Analysis-of-variance and Simultaneous Component Analysis and it enables analysis of metabolomic studies with an experimental design.

Lastly, we describe six approaches to modelling between-trial variances that make use of different combinations of heterogeneity variance parameterizations and priors.