Linked from

The 20 pages that link to Covariance, each with the reason it gives.

• VarianceRelated: Covariance determines how dependence affects the variance of sums.

• Linear regressionRelated: Covariance helps determine fitted slopes and coefficient uncertainty.

• CorrelationRelated: Pearson correlation standardizes covariance by both variables’ standard deviations.

• Uncertainty quantificationRelated: Dependence among uncertain inputs can substantially change propagated output uncertainty.

• Factor analysisRelated: Covariance, like correlation, can encode the dependence factor models explain.

• Error propagationRelated: Correlated inputs contribute cross-terms that independent-input formulas omit.

• Karl PearsonRelated: Covariance supplies the numerator of the Pearson correlation coefficient.

• Statistical learningRelated: Correlated feature changes can reveal which properties belong together.

• Pearson correlation coefficientRelated: Pearson correlation standardizes covariance by both variables’ standard deviations.

• Chebyshev's sum inequalityRelated: The inequality is a finite-sequence counterpart of nonnegative covariance for similarly ordered values.

• DiversificationRelated: Portfolio risk depends on covariances among asset returns, not only each asset’s individual risk.

• Harry MarkowitzRelated: Asset covariances determine whether combining investments reduces or increases portfolio risk.

• Linearity of expectationRelated: Covariance explains why the variance of a sum does not inherit expectation's unconditional additivity.

• Mean–variance analysisRelated: Portfolio variance depends on how asset returns move together, not only on their individual variances.

• Correlation coefficientRelated: Pearson’s coefficient standardizes covariance to make its scale independent of measurement units.

• Bienaymé's identityRelated: The general variance-of-a-sum formula includes covariance terms that independence makes zero.