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Of the following selections, which is not a descriptor of principal component analysis? The first principal...

Of the following selections, which is not a descriptor of principal component analysis?

  1. The first principal account is not suitable for analysis.
  2. Principal components are uncorrelated variables.
  3. The first principal accounts for most of the variability.
  4. Principal component variables are weighted linear combinations of the original variables.
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Answer #1

Option a) is not a descriptor of principal component analysis.

Principal components analysis is a procedure for identifying a smaller number of uncorrelated variables, called “principal components”, from a large set of data. The goal of principal components analysis is to explain the maximum amount of variance with the fewest number of principal components.

The 1st principal component accounts for or "explains" 47.9% of the overall variability; the 2nd one explains 35.4% of it; the 3rd one explains 16.7% of it.

PCA produces linear combinations of the original variables to generate the axes, also known as principal components, or PCs. the constraint that their sum of squares is 1.

Considering all of the above reasons option b, c and d are correct

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