Principal Component Analysis (PCA) Calculator
Perform PCA to reduce dimensionality and find principal components of your data.
Data Summary
Variables: 2
Observations: 10
Principal Components
| PC | Eigenvalue | Variance % | Cumulative % |
|---|---|---|---|
| PC1 | 1.2840 | 96.32% | 96.32% |
| PC2 | 0.0491 | 3.68% | 100.00% |
Eigenvectors (Loadings)
| Variable | PC1 | PC2 |
|---|---|---|
| Var 1 | 0.6779 | -0.7352 |
| Var 2 | 0.7352 | 0.6779 |
Interpretation
The first 2 principal component(s) explain 100.00% of the total variance. Components with eigenvalues > 1 are typically retained (Kaiser criterion).
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Editorial Note
MyCalcBuddy Editorial Team
This page is maintained as an educational calculator reference.
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Formula Source: Standard Mathematical References
by Various
🔄Last reviewed: May 2026
✓Formula checks are based on standard references and internal QA review.