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This document by AKM Bashar from the Statistical Consulting and Analytics Group at the University of South Florida outlines the necessary assumptions to perform Principal Component Analysis (PCA) on a turbidity dataset. It highlights the need for justification of the Gaussian normality assumption, sample size adequacy, linear independence of covariants, multivariate continuous variables, and the identification of outliers. Notably, it discusses the satisfaction of some assumptions while mentioning that certain criteria like linear relationships were not met, raising important considerations for effective data reduction techniques.
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Principal Component Analysis By AKM Bashar Statistical Consulting and Analytics Group (SCAG) University of South Florida
Before doing PCA on this data set, we need to make sure to justify following assumptions: Gaussian (Normality) assumption justification:
Sample size “Adequacy”. Linear Independencies of the co-variants.
PCA (Principal Components Analysis) • Assumptions: • Multiple variables measured in continuous level. (In this data set, this assumption is satisfied • Linear Relationship Between Variables (this particular data set did not satisfy this) • Sample size adequacy. ( not satisfied) • Check for outliers ( No outliers) • Suitable for data reduction
SAMPLING ADEQUECY: SAMPLING ADEQUECY:
Thank you Any questions?
