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The most common measure of variability is variance and the corresponding measure of correlation between two variables is covariance .
These different models for covariance function with some modifications can be incorporated into our mapping strategy.
If this occurs frequently, then MA experiments will be practically useless for studying any properties of mutational covariance .
The matrix formulation of the model produces an estimate that can be easily transformed into genetic covariance and correlations.
Logistic regression was used instead of univariate chi-squares so that covariance among the variables would be taken into account in the analyses.
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