# Chi-squared distribution

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In probability theory and statistics, the chi-squared distribution (also chi-square or χ²-distribution) with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables. A special case of the gamma distribution, it is one of the most widely used probability distributions in inferential statistics, e.g., in hypothesis testing or in construction of confidence intervals. When it is being distinguished from the more general noncentral chi-squared distribution, this distribution is sometimes called the central chi-squared distribution.

The chi-squared distribution is used in the common chi-squared tests for goodness of fit of an observed distribution to a theoretical one, the independence of two criteria of classification of qualitative data, and in confidence interval estimation for a population standard deviation of a normal distribution from a sample standard deviation. Many other statistical tests also use this distribution, like Friedman's analysis of variance by ranks.

## History and name

This distribution was first described by the German statistician Friedrich Robert Helmert in papers of 1875-6,Template:Sfn where he computed the sampling distribution of the sample variance of a normal population. Thus in German this was traditionally known as the Helmertsche ("Helmertian") or "Helmert distribution".

The distribution was independently rediscovered by the English mathematician Karl Pearson in the context of goodness of fit, for which he developed his Pearson's chi-squared test, published in Template:Harv, with computed table of values published in Template:Harv, collected in Template:Harv. The name "chi-squared" ultimately derives from Pearson's shorthand for the exponent in a multivariate normal distribution with the Greek letter Chi, writing -½χ² for what would appear in modern notation as -½xTΣ−1x (Σ being the covariance matrix). The idea of a family of "chi-squared distributions", however, is not due to Pearson but arose as a further development due to Fisher in the 1920s.Template:Sfn

## Definition

If Z1, ..., Zk are independent, standard normal random variables, then the sum of their squares,

$Q\ =\sum _{i=1}^{k}Z_{i}^{2},$ is distributed according to the chi-squared distribution with k degrees of freedom. This is usually denoted as

$Q\ \sim \ \chi ^{2}(k)\ \ {\text{or}}\ \ Q\ \sim \ \chi _{k}^{2}.$ The chi-squared distribution has one parameter: k — a positive integer that specifies the number of degrees of freedom (i.e. the number of Zi’s)

## Characteristics

Further properties of the chi-squared distribution can be found in the box at the upper right corner of this article.

### Probability density function

The probability density function (pdf) of the chi-squared distribution is

$f(x;\,k)={\begin{cases}{\frac {x^{(k/2-1)}e^{-x/2}}{2^{k/2}\Gamma \left({\frac {k}{2}}\right)}},&x\geq 0;\\0,&{\text{otherwise}}.\end{cases}}$ where Γ(k/2) denotes the Gamma function, which has closed-form values for integer k.

For derivations of the pdf in the cases of one, two and k degrees of freedom, see Proofs related to chi-squared distribution.

### Differential equation

The pdf of the chi-squared distribution is a solution to the following differential equation:

$\left\{{\begin{array}{l}2xf'(x)+f(x)(-k+x+2)=0,\\f(1)={\frac {2^{-k/2}}{{\sqrt {e}}\Gamma \left({\frac {k}{2}}\right)}}\end{array}}\right\}$ ### Cumulative distribution function Chernoff bound for the CDF and tail (1-CDF) of a chi-squared random variable with ten degrees of freedom (k = 10)
$F(x;\,k)={\frac {\gamma ({\frac {k}{2}},\,{\frac {x}{2}})}{\Gamma ({\frac {k}{2}})}}=P\left({\frac {k}{2}},\,{\frac {x}{2}}\right),$ where γ(s,t) is the lower incomplete Gamma function and P(s,t) is the regularized Gamma function.

In a special case of k = 2 this function has a simple form:

$F(x;\,2)=1-e^{-{\frac {x}{2}}}$ and the form is not much more complicated for other small even k.

Tables of the chi-squared cumulative distribution function are widely available and the function is included in many spreadsheets and all statistical packages.

Letting $z\equiv x/k$ , Chernoff bounds on the lower and upper tails of the CDF may be obtained. For the cases when $0 (which include all of the cases when this CDF is less than half):

$F(zk;\,k)\leq (ze^{1-z})^{k/2}.$ The tail bound for the cases when $z>1$ , similarly, is

$1-F(zk;\,k)\leq (ze^{1-z})^{k/2}.$ For another approximation for the CDF modeled after the cube of a Gaussian, see under Noncentral chi-squared distribution.

It follows from the definition of the chi-squared distribution that the sum of independent chi-squared variables is also chi-squared distributed. Specifically, if {Xi}i=1n are independent chi-squared variables with {ki}i=1n degrees of freedom, respectively, then Y = X1 + ⋯ + Xn is chi-squared distributed with k1 + ⋯ + kn degrees of freedom.

### Sample mean

The sample mean of n i.i.d. chi-squared variables of degree k is distributed according to a gamma distribution with shape α and scale θ parameters: ${\bar {X}}={\frac {1}{n}}\sum _{i=1}^{n}X_{i}\sim \mathrm {Gamma} \left(\alpha =n\,k/2,\theta =2/n\right)\qquad \mathrm {where} \quad X_{i}\sim \chi ^{2}(k)$ Asymptotically, given that for a scale parameter $\alpha$ going to infinity, a Gamma distribution converges towards a Normal distribution with expectation $\mu =\alpha \cdot \theta$ and variance $\sigma ^{2}=\alpha \,\theta ^{2}$ , the sample mean converges towards: ${\bar {X}}{\xrightarrow {n\to \infty }}N(\mu =k,\sigma ^{2}=2\,k/n)$ ### Entropy

The differential entropy is given by

$h=\int _{-\infty }^{\infty }f(x;\,k)\ln f(x;\,k)\,dx={\frac {k}{2}}+\ln \!\left[2\,\Gamma \!\left({\frac {k}{2}}\right)\right]+\left(1-{\frac {k}{2}}\right)\,\psi \!\left[{\frac {k}{2}}\right],$ where ψ(x) is the Digamma function.

The chi-squared distribution is the maximum entropy probability distribution for a random variate X for which $E(X)=k$ and $E(\ln(X))=\psi \left(k/2\right)+log(2)$ are fixed. Since the chi-squared is in the family of gamma distributions, this can be derived by substituting appropriate values in the Expectation of the Log moment of Gamma. For derivation from more basic principles, see the derivation in moment generating function of the sufficient statistic.

### Noncentral moments

The moments about zero of a chi-squared distribution with k degrees of freedom are given by

$\operatorname {E} (X^{m})=k(k+2)(k+4)\cdots (k+2m-2)=2^{m}{\frac {\Gamma (m+{\frac {k}{2}})}{\Gamma ({\frac {k}{2}})}}.$ ### Cumulants

The cumulants are readily obtained by a (formal) power series expansion of the logarithm of the characteristic function:

$\kappa _{n}=2^{n-1}(n-1)!\,k$ ### Asymptotic properties

By the central limit theorem, because the chi-squared distribution is the sum of k independent random variables with finite mean and variance, it converges to a normal distribution for large k. For many practical purposes, for k > 50 the distribution is sufficiently close to a normal distribution for the difference to be ignored. Specifically, if X ~ χ²(k), then as k tends to infinity, the distribution of $(X-k)/{\sqrt {2k}}$ tends to a standard normal distribution. However, convergence is slow as the skewness is ${\sqrt {8/k}}$ and the excess kurtosis is 12/k.

## Relation to other distributions

File:Chi on SAS.png
Approximate formula for median compared with numerical quantile (top). Difference between numerical quantile and approximate formula (bottom).

A chi-squared variable with k degrees of freedom is defined as the sum of the squares of k independent standard normal random variables.

If Y is a k-dimensional Gaussian random vector with mean vector μ and rank k covariance matrix C, then X = (Yμ)TC−1(Yμ) is chi-squared distributed with k degrees of freedom.

The sum of squares of statistically independent unit-variance Gaussian variables which do not have mean zero yields a generalization of the chi-squared distribution called the noncentral chi-squared distribution.

If Y is a vector of k i.i.d. standard normal random variables and A is a k×k symmetric, idempotent matrix with rank k−n then the quadratic form YTAY is chi-squared distributed with k−n degrees of freedom.

The chi-squared distribution is also naturally related to other distributions arising from the Gaussian. In particular,

• If X1  ~  χ2k1 and X2  ~  χ2k2 are statistically independent, then X1 + X2  ~ χ2k1+k2. If X1 and X2 are not independent, then X1 + X2 is not chi-squared distributed.

## Generalizations

The chi-squared distribution is obtained as the sum of the squares of k independent, zero-mean, unit-variance Gaussian random variables. Generalizations of this distribution can be obtained by summing the squares of other types of Gaussian random variables. Several such distributions are described below.

### Linear combination

If $X_{1},...,X_{n}$ are chi square random variables and $a_{1},...,a_{n}\in \mathbb {R} _{>0}$ , then a closed expression for the distribution of $X=\sum _{i=1}^{n}a_{i}X_{i}$ is not known. It may be, however, calculated using the property of characteristic functions of the chi-squared random variable.

### Chi-squared distributions

#### Noncentral chi-squared distribution

{{#invoke:main|main}} The noncentral chi-squared distribution is obtained from the sum of the squares of independent Gaussian random variables having unit variance and nonzero means.

#### Generalized chi-squared distribution

{{#invoke:main|main}} The generalized chi-squared distribution is obtained from the quadratic form z′Az where z is a zero-mean Gaussian vector having an arbitrary covariance matrix, and A is an arbitrary matrix.

### Gamma, exponential, and related distributions

The chi-squared distribution X ~ χ²(k) is a special case of the gamma distribution, in that X ~ Γ(k/2, 1/2) using the rate parameterization of the gamma distribution (or X ~ Γ(k/2, 2) using the scale parameterization of the gamma distribution) where k is an integer.

Because the exponential distribution is also a special case of the Gamma distribution, we also have that if X ~ χ²(2), then X ~ Exp(1/2) is an exponential distribution.

The Erlang distribution is also a special case of the Gamma distribution and thus we also have that if X ~ χ²(k) with even k, then X is Erlang distributed with shape parameter k/2 and scale parameter 1/2.

## Applications

The chi-squared distribution has numerous applications in inferential statistics, for instance in chi-squared tests and in estimating variances. It enters the problem of estimating the mean of a normally distributed population and the problem of estimating the slope of a regression line via its role in Student’s t-distribution. It enters all analysis of variance problems via its role in the F-distribution, which is the distribution of the ratio of two independent chi-squared random variables, each divided by their respective degrees of freedom.

Following are some of the most common situations in which the chi-squared distribution arises from a Gaussian-distributed sample.

• The box below shows some statistics based on Xi ∼ Normal(μi, σ2i), i = 1, ⋯, k, independent random variables that have probability distributions related to the chi-squared distribution:

The chi-squared distribution is also often encountered in Magnetic Resonance Imaging .

## Table of χ2 value vs p-value

The p-value is the probability of observing a test statistic at least as extreme in a chi-squared distribution. Accordingly, since the cumulative distribution function (CDF) for the appropriate degrees of freedom (df) gives the probability of having obtained a value less extreme than this point, subtracting the CDF value from 1 gives the p-value. The table below gives a number of p-values matching to χ2 for the first 10 degrees of freedom.

A low p-value indicates greater statistical significance, i.e. greater confidence that the observed deviation from the null hypothesis is significant. A p-value of 0.05 is often used as a bright-line cutoff between significant and not-significant results.

Degrees of freedom (df) χ2 value
1
0.004 0.02 0.06 0.15 0.46 1.07 1.64 2.71 3.84 6.64 10.83
2
0.10 0.21 0.45 0.71 1.39 2.41 3.22 4.60 5.99 9.21 13.82
3
0.35 0.58 1.01 1.42 2.37 3.66 4.64 6.25 7.82 11.34 16.27
4
0.71 1.06 1.65 2.20 3.36 4.88 5.99 7.78 9.49 13.28 18.47
5
1.14 1.61 2.34 3.00 4.35 6.06 7.29 9.24 11.07 15.09 20.52
6
1.63 2.20 3.07 3.83 5.35 7.23 8.56 10.64 12.59 16.81 22.46
7
2.17 2.83 3.82 4.67 6.35 8.38 9.80 12.02 14.07 18.48 24.32
8
2.73 3.49 4.59 5.53 7.34 9.52 11.03 13.36 15.51 20.09 26.12
9
3.32 4.17 5.38 6.39 8.34 10.66 12.24 14.68 16.92 21.67 27.88
10
3.94 4.87 6.18 7.27 9.34 11.78 13.44 15.99 18.31 23.21 29.59
P value (Probability)
0.95 0.90 0.80 0.70 0.50 0.30 0.20 0.10 0.05 0.01 0.001