Sentence examples for variance from the data from inspiring English sources

Axes I and II account for 72.8 % of total variance from the data set (57.6 and 15.2 % for axes I and II, respectively).

First, this factor analysis is applied to derive maximum variance from the data set with individual components; hence, it serves to decrease a greater number of variables into a fewer number of components (Tabachnick and Fidell 1996).

The objective of using PCA and of this study is to extract maximum variance from the data set, to reduce a large number of variables down to a smaller number of components, and to initiate factorial analysis to reveal the nature of component factors (Tabachnick & Fidell 2012).

The linear combination that extracts the maximum variance from the data is termed the principal component.

Approximating RMLA with RRWA uses preliminary estimates of the heritability instead of estimating the genetic and the residual variance from the data set under investigation.

While we cannot estimate Z and e directly, we can estimate their variances from the data.

Another advantage of using pre-computed SNP variances from the data rather than using variances that are a priori distributed across the SNPs, is that the SNP variances used are not very dependent on assumptions that need to be made in RR-BLUP, where the variance for all SNPs is assumed equal and simply computed as the total genetic variance divided by the number of SNPs.

It was not possible to calculate variance values from the data given by Pyke [5] to test whether the differences in 1978 were significant or not.

The estimation of the genetic and error variance components from the data set under consideration can be replaced by using preliminary estimates of the heritability h p 2 as suggested by Hofheinz et al. (2012).

For one, factor analysis has an array of rotations and extractions to choose from, given the same amount of variance from the original data.

However, researchers often account for nearly all of the variance from the original data using only the first few, or lower-order principal components (PCs), which are often associated with the most dominant movement patterns.