Explanation:
Option C is correct because a scree plot in PCA displays the eigenvalues in descending order, which helps determine how many principal components to retain. The plot shows the variance explained by each principal component, with eigenvalues on the y-axis and component number on the x-axis.
Option A is incorrect because PCA transforms correlated features into uncorrelated principal components, not the other way around. The principal components are orthogonal (uncorrelated) linear combinations of the original variables.
Option B is incorrect because eigenvectors represent the directions of maximum variance, while eigenvalues give the proportion of total variance explained by each principal component. The proportion of variance explained is calculated as: eigenvalue / sum of all eigenvalues.
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Which of the following statements about principal components analysis (PCA) is most accurate?
A
PCA transforms uncorrelated features of data into a few main, correlated composite variables.
B
An eigenvector gives the proportion of total variance in the initial data that is explained by each eigenvalue.
C
A scree plot is used to show the eigenvectors ordered from highest to lowest according to their eigenvalues.
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