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Principal Component Analysis

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”.

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Principal Component Analysis

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  1. Principal Component Analysis By AKM Bashar Statistical Consulting and Analytics Group (SCAG) University of South Florida

  2. Before doing PCA on this data set, we need to make sure to justify following assumptions: Gaussian (Normality) assumption justification:

  3. Sample size “Adequacy”. Linear Independencies of the co-variants.

  4. 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

  5. ANAYSIS OF TURBIDITY:

  6. SAMPLING ADEQUECY: SAMPLING ADEQUECY:

  7. Thank you Any questions?

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