Sentence examples for variance of variables from inspiring English sources
The Snaer program calculates the posterior mean and variance of variables on some of which we have data (with precisions), on some we have prior information (with precisions), and on some prior indicator ratios (with precisions) are available.
PCA analyzes the variance of variables and reorganizes it into a new set of uncorrelated independent components (principal components) equal to the number of original variables as linear combinations of the measured variables (Swan and Sandilands 1995).
The explanatory performance of a model is evaluated using the R² coefficient which corresponds to the part of the variance of variables Y explained by the variables X.
Hence, Y1 is defined as the first principal component; in contrast, the variance of variables is smaller in Y2 axis, and it can explain minor information relative to Y1, so Y2 is called the second principal component.
An unsupervised hierarchical clustering analysis was applied to analyze the similarities in MMSDK and DGE profiles across the five TAMR cell model lines using Qlucore Omics Explorer 2.3 software with a data filter requiring that the variance/maximum variance of variables across samples is higher than 0.001.
Here E(X and σ denote the expected value and the variance of variable X, respectively.
To visualize and explore the patterns behind co-dependence and co-variance of variables in our dataset, and to better measure common features that underlie these correlations, we used principal component analysis (PCA).
Tukey's test was applied when variances of variables were normal and Dunnet's test when they were not.
Now return to Figure 3 and notice that the variances of variables η3A, η3B and η3C differ; with η3A having the smallest variance and η3C the largest variance because the variance-producing causal actions of additional "error" variables impinge on the chain of latent variables in moving from η3A toward η3C.
The study also examines generalized error variance decomposition of variables due to various shocks in the system.
The residual plot analysis showed constant variance, independence of variables, and normality of the distribution.
