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cs.pca-variance-explained Calculator
Calculates cumulative variance explained by principal components from eigenvalues, showing how many components are needed to capture 80%, 90%, and 95% of variance. Choosing the number of PCA components is a bias-variance trade-off — this makes the elbow point explicit.
Inputs
Lambda1
Reference formula or conversion factor shown for context.
Lambda2
Reference formula or conversion factor shown for context.
Lambda3
Reference formula or conversion factor shown for context.
Lambda4
Reference formula or conversion factor shown for context.
Lambda5
Reference formula or conversion factor shown for context.
Results
variance explained by PC1
Standard deviation squared — average squared deviation from the mean. Essential for ANOVA, regression, and many statistical tests, though the squared units make it less intuitive directly.
cumulative variance PC1+PC2
Standard deviation squared — average squared deviation from the mean. Essential for ANOVA, regression, and many statistical tests, though the squared units make it less intuitive directly.
cumulative variance PC1-PC3
Standard deviation squared — average squared deviation from the mean. Essential for ANOVA, regression, and many statistical tests, though the squared units make it less intuitive directly.
PCs for 80% variance
Standard deviation squared — average squared deviation from the mean. Essential for ANOVA, regression, and many statistical tests, though the squared units make it less intuitive directly.
scree plot elbow
Reference formula or conversion factor shown for context.