quantity

variance

A statistical measure of the spread of data points around the mean, indicating variability.
11 related articles · 5 formulas

Description

Variance is a statistical measure that quantifies how much a set of numbers spread out from their average (mean). It is calculated as the average of the squared differences from the mean. A high variance indicates that the data points are widely scattered, while a low variance means they cluster closely around the mean. In physics, variance is used to describe noise, measurement errors, and the distribution of physical quantities like energy or velocity.

History

The concept of variance was formalized by Ronald Fisher in 1918, building on earlier work by Carl Friedrich Gauss on least squares and by Francis Galton on regression.

How it works

For a dataset with N values x_i and mean μ, the variance σ² is given by σ² = (1/N) Σ (x_i - μ)². The square root of variance is the standard deviation (σ), which has the same units as the original data and is often more intuitive. Variance is additive for independent random variables, making it a fundamental tool in error propagation and statistical mechanics.

In practice

Variance is used in experimental physics to quantify measurement uncertainty, in quantum mechanics to describe the spread of observable values, and in cosmology to analyze the distribution of galaxies.

Fun fact

In quantum mechanics, the Heisenberg uncertainty principle is often expressed in terms of variances: the product of the variances of position and momentum must be at least (ħ/2)².

Concept graph →

Formulas

$$\mathrm{Var}(X + Y) = \mathrm{Var}(X) + \mathrm{Var}(Y)$$
Variance of sum equals sum of variances for independent variables.
What is in the formula
Xvariablefirst random variable — random variable
Yvariablesecond random variable — random variable
Var(X)variablevariance of X — variance
Var(Y)variablevariance of Y — variance
Var(X+Y)variablevariance of the sum X+Y — variance
$$k(r) = rac{\sigma^2 2^{1-\nu}}{\Gamma(\nu)} \left( \frac{\sqrt{2\nu} r}{l} \right)^\nu K_\nu \left( \frac{\sqrt{2\nu} r}{l} \right)$$
A covariance function for Gaussian processes with smoothness parameter ν.
What is in the formula
rvariabledistance between two points — distance metre
νvariablesmoothness parameter
lvariablelength scale parameter — length metre
σvariablevariance amplitude — variance square metre
ΓoperatorGamma function, generalizes factorial — gamma function
K_νoperatorModified Bessel function of the second kind of order ν — modified bessel function second kind
$$x_t = \phi x_{t-1} + \epsilon_t, \; \epsilon_t \sim \mathcal{N}(0,\sigma^2)$$
A time series where each value depends on the previous plus noise.
What is in the formula
x_tvariablevalue of the process at time t — time series value
x_{t-1}variablevalue of the process at previous time — time series value
\phivariableautoregressive coefficient — autoregressive coefficient
\epsilon_tvariablewhite noise innovation at time t — noise term
\sigma^2variablevariance of the noise — variance
$$\sigma_{\text{total}}^2 = \sigma_1^2 + \sigma_2^2$$
The variance of a sum is the sum of the variances for independent variables.
What is in the formula
\sigma_{\text{total}variableVariance of the sum of two variables — variance
\sigma_1^2variableVariance of the first variable — variance
\sigma_2^2variableVariance of the second variable — variance
$$\text{Var}(X_n) \sim f(d_s)$$
Variance scaling with spectral dimension.
What is in the formula
X_nvariablerandom variable indexed by n — random variable
d_svariablespectral dimension — spectral dimension
Varoperatorstatistical variance of a random variable — variance
~operatorasymptotically proportional to (up to a constant factor) — asymptotic equivalence
foperatorsome function of the spectral dimension — function

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