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The 63 pages that link to Variance, each with the reason it gives.

• Random variableRelated: It measures the spread of values around the random variable's expectation.

• Signal-to-noise ratioRelated: Variance measures noise strength when fluctuations are modeled as zero-mean random quantities.

• Probability distributionRelated: It measures how widely the distribution spreads around its expected value.

• Normal distributionRelated: It is the normal distribution's second parameter and controls its spread.

• Binomial distributionRelated: Its value is the trial count times the success probability times the failure probability.

• Central limit theoremRelated: The classical normalization uses variance to set the scale of a sum.

• Cauchy–Schwarz inequalityRelated: Applied to centered variables, the inequality bounds covariance by the product of standard deviations.

• Law of large numbersRelated: Variance assumptions help control how far sample averages may stray from expectation.

• Arithmetic meanRelated: The mean supplies the reference point for this measure of spread.

• Poisson distributionRelated: Its equality with the mean is a defining feature of the Poisson model.

• Random walkRelated: Step variance controls the typical growth of a walk's displacement.

• CovarianceRelated: Variance is covariance of a variable with itself.

• Probability mass functionRelated: The mass function determines the probabilities used to calculate spread.

• Standard errorRelated: Standard error is the square root of a statistic’s sampling variance.

• StandardizationRelated: Its square root supplies the scale used in standardization.

• Risk aversionRelated: It is one common measure of the spread that risk-averse choices penalize.

• Geometric distributionRelated: Its value, (1−p)/p², quantifies the spread in waiting times.

• Strong law of large numbersRelated: Variance conditions help control how far partial averages stray from their target.

• Bernoulli distributionRelated: For a Bernoulli variable, the variance is p(1−p).

• Error propagationRelated: Independent variances add directly when the calculation is a sum.

• Beta distributionRelated: The two parameters jointly determine the distribution’s spread around its mean.

• Gaussian distributionRelated: Variance determines the Gaussian distribution’s spread around its center.

• Method of momentsRelated: Matching sample and theoretical variance can identify parameters beyond the mean.

• Ordinary least squaresRelated: The Gauss–Markov result compares OLS variance with that of other linear unbiased estimators.

• Hypergeometric distributionRelated: Finite-population dependence reduces its variance relative to independent sampling.

• Modern portfolio theoryRelated: The framework uses return variance as its basic measure of investment risk.

• Negative binomial distributionRelated: It quantifies the spread in failure counts around their expected value.

• Prediction intervalRelated: The model’s variance components govern expected spread across studies.

• Concentration inequalityRelated: It quantifies spread and determines the scale in several classic bounds.

• Harry MarkowitzRelated: Markowitz used return variance as a mathematical measure of investment risk.

• Mean–variance analysisRelated: It measures return dispersion and serves as the basic risk measure in the analysis.

• Weighted averageRelated: Variance helps determine weights when measurements have differing precision.

• Chi-squared distributionRelated: Sums of squared normal deviations connect the distribution to inference about variance.

• Log-normal distributionRelated: Its value depends on both parameters of the underlying normal distribution.

• Sum of squaresRelated: Sample variance is obtained by scaling the deviations' sum of squares.

• Gauss–Markov theoremRelated: “Best” means having the smallest estimator variance within a specified class.

• Discrete uniform distributionRelated: Equal probabilities make its variance a scaled sum of squared deviations across the support.

• Kolmogorov's three-series theoremRelated: The theorem sums variances of the truncated variables to control accumulated fluctuation.

• Law of the iterated logarithmRelated: Finite, nonzero variance sets the scale in the classical identically distributed theorem.

• Regression toward the meanRelated: Variation between measurements leaves room for an extreme result to be followed by a less extreme one.

• Karamata's inequalityRelated: Squared deviation is convex, so equal-mean vectors ordered by majorization have ordered sums of squares and variances.

• Continuous uniform distributionRelated: The interval’s width determines spread, with variance equal to its squared width divided by twelve.

• De Moivre–Laplace theoremRelated: The binomial variance sets the scale of fluctuations used in the approximation.

• Kolmogorov's inequalityRelated: Independence makes the partial sum's total variance the bound's numerator.

• Kolmogorov's two-series theoremRelated: Summable variances bound the cumulative fluctuations around the means.

• Precision (metrology)Related: It quantifies measurement dispersion in squared units and underlies standard deviation.

• Fluctuations and noiseRelated: It quantifies the magnitude of fluctuations around a typical value.

• Ibragimov–Iosifescu conjectureRelated: A central limit theorem for sums requires their variance growth to support a nondegenerate normalization.