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STATISTICS
Multivariate Analysis
Latent Factors
Hidden forces in data reveal surprising patterns shaping our understanding of complex systems
12 days ago
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Why are latent factors crucial in factor analysis when interpreting observed variables?
Because they are directly measured variables that replace observed data entirely.
Because they represent underlying variables that explain correlations among observed variables, reducing complexity.
Because they are random errors that do not influence the observed variables.
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STATISTICS
Multivariate Analysis
Factor Analysis
Invisible forces behind data patterns reveal simpler truths beneath complexity
9 Feb 2026
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What is the primary benefit of using factor analysis on a dataset with many correlated variables?
It eliminates all errors in the observed variables to produce perfect measurements.
It identifies a smaller number of unobserved factors that explain the correlations among observed variables.
It treats each observed variable independently without considering their correlations.
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