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Principal component analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it be orthogonal to (i.e., uncorrelated with) the preceding components.



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Matrix Sizes up to 1 Million (iPhone 4s, new iPad) tested

Up to 250 samples

Decimal separator determinable

Supported Data formats:
Tab-delimited text-files or comma-separated text files

Color coding of metadata

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Support of descriptive data

Statistical testing of descriptive data to reveal significant principal components

iCloud Sync

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Auto-saving of calculations and syncing to iCloud


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Import of data files from Dropbox