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The chi-squared distribution (also written χ²) is a sampling distribution derived from the normal distribution. Suppose we have a random sample of size n from a normal(μ, σ²) distribution, with sample variance S². The statistic (n − 1)S²/σ² has a chi-squared distribution with ν = n − 1 degrees of freedom.

The function Γ(s) in the pdf and cdf denotes the gamma function, while the function γ(s, x) in the cdf denotes the lower incomplete gamma function

Parameter Range Description
ν ν = 1, 2, ... Degrees of freedom

Probability Density Function

f ( x ; ν ) = 1 Γ ( ν / 2 ) 2 ν / 2 x ν / 2 1 e x / 2
F ( x ; ν ) = γ ( ν / 2 , x / 2 ) Γ ( ν / 2 )

Support

0 x <

Mean

Variance

Example ν
Z is a standard normal random variable. Let X = Z². 1.000
A dart is thrown at a dart board. The probability distribution of hits is centered on the bullseye, and has independent standard normal distributions vertically and horizontally. Let X be the square of the distance of a hit from the bullseye. 2.000
Y₁, Y₂, ..., Y₁₀ is a sample taken from a normal(0, 4) distribution, with sample variance S². Let X = 9S²/4. 9.000

X ∼ Chi-Squared(ν)

ν =
Chart of the chi-squared distribution Chart area for displaying the chi-squared pdf, cdf, and simulation

E(X) = , Var(X) =

The chi-squared distribution can be used to estimate a confidence interval for the true population variance σ² of a normally distributed population based on a sample variance S². The pdf is strictly decreasing for ν ≤ 2 and unimodel for ν > 2.

The chi-squared distribution also arises naturally as the probability distribution of the sum of the squares of n independent standard normal random variables. This sum will have a chi-squared(n) distribution.

The chi-squared distribution can be used to estimate a confidence interval for the true population variance σ² of a normally distributed population based on a sample variance S².

The chi-squared distribution also arises naturally as the probability distribution of the sum of the squares of n independent standard normal random variables. This sum will have a chi-squared(n) distribution.

The illustration above shows a sample of n points (marked red) chosen independently and at random from a normal(μ, σ2) distribution. The random variable X = (n − 1)S22 has a chi-squared(n − 1) distribution, where S2 is the sample variance.

The simulation above shows a sample of n points (marked red) chosen independently and at random from a normal(μ, σ2) distribution. The light blue line shows the value of X = (n − 1)S22, where S2 is the sample variance. The random variable X has a chi-squared(n − 1) distribution. The histogram accumulates the results of each simulation.

Y = X₁/(X₁ + X₂) ∼ Beta(ν₁/2, ν₂/2) ∑ Xᵢ ∼ Chi-Squared(Σνᵢ) Chi-Squared(2) ∼ Exponential(2) (X₁/ν₁)/(X₂/ν₂) ∼ F(ν₁, ν₂)

Chart of the related distribution Chart area for displaying the related pdf, cdf, and simulation

E(Y) = , Var(Y) =