Exact(2)
Each principal component represents a linear combination of the original variables with the first principal component defined as the linear combination with maximal sample variance among all linear combinations of the variables.
The next principal component represents the linear combination that explains the maximal sample variance that remains unexplained by the first with the additional condition that it is orthogonal to the first [ 27].
Similar(58)
PCA of the protein area measurements of this dataset revealed that the two maximal and important directions of sample variance, PCs 1 and 2 accounted for 90.35% of the total sample variance.
Characteristics of the sample such as the sample mean, the sample variance, and the sample proportion are called sample statistics.
The sample variance of the sample is defined as (1.2).
We provide bounds for tail probabilities of the sample variance.
Is this just the sample variance?
The within sample variance is estimated from the variance of the centroid vector as.
The variance of the estimate was the sample variance divided by the number of patients.
Using sample variances for estimating a variance function is intuitively more appealing than using residuals.
Furthermore, as sample size decreases these sample variances increase.
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