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The MADi component corresponds then to i-th difference bearing the maximum variance between two sets of variables that are positively correlated.
For all investigated markers (except D-loop and G3PDH) the three-population-model showed the maximum variance between groups and the lowest among populations within each group (Table 2).
It determines the linear combinations of sensor values so that the maximum variance between all data points can be obtained in mutually orthogonal dimensions.
Principal component analysis determines the linear combinations of the input values such that the maximum variance between all data points can be obtained in mutually orthogonal dimensions.
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In this case, the maximum variance criterion should be used.
To do so, PCA considers the maximum variance in the dataset, whereas MAD considers maximum autocorrelation, since it takes into account the maximum variance of the difference images.
The first PCA score attains the maximum variance among the linear combination of the three scales.
The linear combination that extracts the maximum variance from the data is termed the principal component.
The PCs are mutually orthogonal and their extraction is such that the first PC holds the maximum variance, the second holds the second-maximum variance, and so on.
The mapping of sample set in low dimension space has the maximum variance.
The maximum Variance Inflation Factor (VIF) did not exceed 3.9 so that multicollinearity may be neglected.
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CEO of Professional Science Editing for Scientists @ prosciediting.com