chisquared
Probability density function

Cumulative distribution function

Notation

\chi^2(k)\! or \chi^2_k\!

Parameters

k \in \mathbb{N}_{>0}~~ (known as "degrees of freedom")

Support

x ∈ [0, +∞)

PDF

\frac{1}{2^{\frac{k}{2}}\Gamma\left(\frac{k}{2}\right)}\; x^{\frac{k}{2}1} e^{\frac{x}{2}}\,

CDF

\frac{1}{\Gamma\left(\frac{k}{2}\right)}\;\gamma\left(\frac{k}{2},\,\frac{x}{2}\right)

Mean

k

Median

\approx k\bigg(1\frac{2}{9k}\bigg)^3

Mode

max{ k − 2, 0 }

Variance

2k

Skewness

\scriptstyle\sqrt{8/k}\,

Ex. kurtosis

3 + 12 / k

Entropy

\begin{align}\frac{k}{2}&+\ln(2\Gamma(k/2)) \\ &\!+(1k/2)\psi(k/2)\end{align}

MGF

(1 − 2 t)^{−k/2} for t < ½

CF

(1 − 2 i t)^{−k/2} ^{[1]}

In probability theory and statistics, the chisquared distribution (also chisquare or χ²distribution) with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables. It is a special case of the gamma distribution and is one of the most widely used probability distributions in inferential statistics, e.g., in hypothesis testing or in construction of confidence intervals.^{[2]}^{[3]}^{[4]}^{[5]} When it is being distinguished from the more general noncentral chisquared distribution, this distribution is sometimes called the central chisquared distribution.
The chisquared distribution is used in the common chisquared 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.
Contents

Definition 1

Introduction to the chisquared distribution 2

Where are the squared normal distributions? 2.1

Characteristics 3

Probability density function 3.1

Differential equation 3.2

Cumulative distribution function 3.3

Additivity 3.4

Sample mean 3.5

Entropy 3.6

Noncentral moments 3.7

Cumulants 3.8

Asymptotic properties 3.9

Relation to other distributions 4

Generalizations 5

Linear combination 5.1

Chisquared distributions 5.2

Noncentral chisquared distribution 5.2.1

Generalized chisquared distribution 5.2.2

Gamma, exponential, and related distributions 5.3

Applications 6

Table of χ2 value vs pvalue 7

History and name 8

See also 9

References 10

Further reading 11

External links 12
Definition
If Z_{1}, ..., Z_{k} 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 chisquared distribution with k degrees of freedom. This is usually denoted as

Q\ \sim\ \chi^2(k)\ \ \text{or}\ \ Q\ \sim\ \chi^2_k .
The chisquared distribution has one parameter: k — a positive integer that specifies the number of degrees of freedom (i.e. the number of Z_{i}’s)
Introduction to the chisquared distribution
The chisquared distribution is used primarily in hypothesis testing. Unlike more widelyknown distributions such as the normal distribution and the exponential distribution, the chisquared distribution is rarely used to model natural phenomena. It arises in the following hypothesis tests, among others.
It is also a component of the definition of the tdistribution and the Fdistribution used in ttests, analysis of variance, and regression analysis.
The primary reason that the chisquared distribution is used extensively in hypothesis testing is its relationship to the normal distribution. Many hypothesis tests use a test statistic, such as the t statistic in a ttest. For these hypothesis tests, as the sample size, n, increases, the sampling distribution of the test statistic approaches the normal distribution (Central Limit Theorem). Because the test statistic (such as t) is asymptotically normally distributed, provided the sample size is sufficiently large, the distribution used for hypothesis testing may be approximated by a normal distribution. Testing hypotheses using a normal distribution is well understood and relatively easy. The simplest chisquared distribution is the square of a standard normal distribution. So wherever a normal distribution could be used for a hypothesis test, a chisquared distribution could be used.
Specifically, suppose that Z is a standard normal random variable, with mean = 0 and variance = 1. Z ~ N(0,1). A sample drawn at random from Z is a sample from the distribution shown in the graph of the standard normal distribution. Define a new random variable Q. To generate a random sample from Q, take a sample from Z and square the value. The distribution of the squared values is given by the random variable Q = Z^{2}. The distribution of the random variable Q is an example of a chisquared distribution: \ Q\ \sim\ \chi^2_1 . The subscript 1 indicates that this particular chisquared distribution is constructed from only 1 standard normal distribution. A chisquared distribution constructed by squaring a single standard normal distribution is said to have 1 degree of freedom. Thus, as the sample size for a hypothesis test increases, the distribution of the test statistic approaches a normal distribution. and the distribution of the square of the test statistic approaches a chisquared distribution. Just as extreme values of the normal distribution have low probability (and give small pvalues), extreme values of the chisquared distribution have low probability.
An additional reason that the chisquared distribution is widely used is that it is a member of the class of likelihood ratio tests (LRT).^{[6]} LRT's have several desirable properties; in particular, LRT's commonly provide the highest power to reject the null hypothesis (Neyman–Pearson lemma). However, the normal and chisquared approximations are only valid asymptotically. For this reason, it is preferable to use the t distribution rather than the normal approximation or the chisquared approximation for small sample size. Similarly, in analyses of contingency tables, the chisquared approximation will be poor for small sample size, and it is preferable to use the Fisher Exact test. Ramsey and Ramsey show that the exact binomial test is always more powerful than the normal approximation.^{[7]}
Where are the squared normal distributions?
If the chisquared distribution is used because it is the sum of squared normal distributions, where are the squared normal distributions in contingency tables analyzed with a chisquared test? The answer can be traced back to the normal approximation to the binomial distribution. Consider an experiment in which 10 fair coins are tossed, and the number of heads is observed. This experiment can be modeled with a binomial distribution, with n=10 trials and p = 0.5 probability of heads on each trial. Suppose that heads is observed 1 times in 10 trials. What is the probability of a result as extreme as 1 heads in 10 trials, if the probability of heads is p=0.5?
Three methods to determine the probability are:

Calculate the probability exactly using the binomial distribution.

Estimate the probability using normal approximation to the binomial distribution.

Estimate the probability using a chisquared test. This result will be the same as the result for the normal approximation.
Calculation using the exact binomial and the normal approximation may be performed using http://vassarstats.net/binomialX.html. Calculation of the chisquare probability may be performed using http://vassarstats.net/csfit.html.
Using the binomial distribution, the probability of a result as extreme 1 heads in 10 trials is the sum of the probabilities of 0 heads, 1 head, 9 heads, or 10 heads. Notice that this is a twotailed or twosided test. This test gives p=0.0215.
Using the normal approximation to the binomial distribution, the (twosided) probability of a result as extreme 1 heads in 10 trials is p=0.0271.
The chisquared test is performed as follows. The observed number of heads is 1, and the observed number of tails is 9. The expected number of heads = expected number of tails = 10*0.5 = 5. The difference between the observed and expected is 15=4 for heads, and 95=4 for tails. The chisquared statistic (with Yates's correction for continuity) is

\chi^2 = {(1  50.5)^2 \over 5} + {(9  50.5)^2 \over 5} = 4.9.
For the chisquared test, the (twosided) probability of a result as extreme as 1 heads in 10 trials is p=0.027, the same as the result using the normal approximation. That is, the probability that the chisquared statistic with one degree of freedom is greater than 4.9 is p=0.027.
Lancaster ^{[8]} shows the connections among the binomial, normal, and chisquared distributions, as follows. De Moivre and Laplace established that a binomial distribution could be approximated by a normal distribution. Specifically they showed the asymptotic normality of the random variable

\chi = {(m  Np)\over \sqrt{(Npq)}}
where m is the observed number of success in N trials, where the probability of success is p, and q = 1 − p.
Squaring both sides of the equation gives

\chi^2 = {(m  Np)^2\over (Npq)}
Using N = Np + N(1 − p), N = m + (N − m), and q = 1 − p, this equation simplifies to

\chi^2 = {(m  Np)^2\over (Np)} + {(N  m  Nq)^2\over (Nq)}
The expression on the right is of the form that Pearson would generalize to the form:

\chi^2 = \sum_{i=1}^{n} \frac{(O_i  E_i)^2}{E_i}
where

\chi^2 = Pearson's cumulative test statistic, which asymptotically approaches a \chi^2 distribution.

O_i = the number of observations of type i.

E_i = N p_i = the expected (theoretical) frequency of type i, asserted by the null hypothesis that the fraction of type i in the population is p_i

n = the number of cells in the table.
In the case of a binomial outcome (flipping a coin), the binomial distribution may be approximated by a normal distribution (for sufficiently large n). Because the square of a standard normal distribution is the chisquared distribution with one degree of freedom, the probability of a result such as 1 heads in 10 trials can be approximated either by the normal or the chisquared distribution. However, many problems involve more than the two possible outcomes of a binomial, and instead require 3 or more categories, which leads to the multinomial distribution. Just as de Moivre and Laplace sought for and found the normal approximation to the binomial, Pearson sought for and found a multivariate normal approximation to the multinomial distribution. Pearson showed that the chisquared distribution, the sum of multiple normal distributions, was such an approximation to the multinomial distribution ^{[8]}
Characteristics
Further properties of the chisquared 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 chisquared distribution is

f(x;\,k) = \begin{cases} \frac{x^{(k/21)} e^{x/2}}{2^{k/2} \Gamma\left(\frac{k}{2}\right)}, & x > 0; \\ 0, & \text{otherwise}. \end{cases}
where Γ(k/2) denotes the Gamma function, which has closedform values for integer k.
For derivations of the pdf in the cases of one, two and k degrees of freedom, see Proofs related to chisquared distribution.
Differential equation
The pdf of the chisquared distribution is a solution to the following differential equation:

\left\{\begin{array}{l} 2 x f'(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 (1CDF) of a chisquared random variable with ten degrees of freedom (
k = 10)
Its cumulative distribution function is:

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 chisquared 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.^{[9]} For the cases when 0 < z < 1 (which include all of the cases when this CDF is less than half):

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

1F(z k;\,k) \leq (z e^{1z})^{k/2}.
For another approximation for the CDF modeled after the cube of a Gaussian, see under Noncentral chisquared distribution.
Additivity
It follows from the definition of the chisquared distribution that the sum of independent chisquared variables is also chisquared distributed. Specifically, if {X_{i}}_{i=1}^{n} are independent chisquared variables with {k_{i}}_{i=1}^{n} degrees of freedom, respectively, then Y = X_{1} + ⋯ + X_{n} is chisquared distributed with k_{1} + ⋯ + k_{n} degrees of freedom.
Sample mean
The sample mean of n i.i.d. chisquared variables of degree k is distributed according to a gamma distribution with shape \alpha and scale \theta parameters:

\bar X = \frac{1}{n} \sum_{i=1}^{n} X_i \sim \operatorname{Gamma}\left(\alpha=n\, k /2, \theta= 2/n \right) \qquad \text{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 )
Note that we would have obtained the same result invoking instead the central limit theorem, noting that for each chisquared variable of degree k the expectation is k , and its variance 2\,k (and hence the variance of the sample mean \bar X being \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 chisquared 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 chisquared 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 chisquared distribution with k degrees of freedom are given by^{[10]}^{[11]}

\operatorname{E}(X^m) = k (k+2) (k+4) \cdots (k+2m2) = 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^{n1}(n1)!\,k
Asymptotic properties
By the central limit theorem, because the chisquared 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.^{[12]} Specifically, if X ~ χ²(k), then as k tends to infinity, the distribution of (Xk)/\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.

The sampling distribution of ln(χ^{2}) converges to normality much faster than the sampling distribution of χ^{2},^{[13]} as the logarithm removes much of the asymmetry.^{[14]} Other functions of the chisquared distribution converge more rapidly to a normal distribution. Some examples are:

If X ~ χ²(k) then \scriptstyle\sqrt{2X} is approximately normally distributed with mean \scriptstyle\sqrt{2k1} and unit variance (result credited to R. A. Fisher).

If X ~ χ²(k) then \scriptstyle\sqrt[3]{X/k} is approximately normally distributed with mean \scriptstyle 12/(9k) and variance \scriptstyle 2/(9k) .^{[15]} This is known as the Wilson–Hilferty transformation.
Relation to other distributions
Approximate formula for median compared with numerical quantile (top). Difference between numerical quantile and approximate formula (bottom).

As k\to\infty, (\chi^2_kk)/\sqrt{2k} ~ \xrightarrow{d}\ N(0,1) \, (normal distribution)

\chi_k^2 \sim {\chi'}^2_k(0) (Noncentral chisquared distribution with noncentrality parameter \lambda = 0 )

If X \sim \mathrm{F}(\nu_1, \nu_2) then Y = \lim_{\nu_2 \to \infty} \nu_1 X has the chisquared distribution \chi^2_{\nu_{1}}

As a special case, if X \sim \mathrm{F}(1, \nu_2)\, then Y = \lim_{\nu_2 \to \infty} X\, has the chisquared distribution \chi^2_{1}

\\boldsymbol{N}_{i=1,...,k}{(0,1)}\^2 \sim \chi^2_k (The squared norm of k standard normally distributed variables is a chisquared distribution with k degrees of freedom)

If X \sim {\chi}^2(\nu)\, and c>0 \,, then cX \sim {\Gamma}(k=\nu/2, \theta=2c)\,. (gamma distribution)

If X \sim \chi^2_k then \sqrt{X} \sim \chi_k (chi distribution)

If X \sim \chi^2 \left( 2 \right), then X \sim \mathrm{Exp(1/2)} is an exponential distribution. (See Gamma distribution for more.)

If X \sim \mathrm{Rayleigh}(1)\, (Rayleigh distribution) then X^2 \sim \chi^2(2)\,

If X \sim \mathrm{Maxwell}(1)\, (Maxwell distribution) then X^2 \sim \chi^2(3)\,

If X \sim \chi^2(\nu) then \tfrac{1}{X} \sim \mbox{Inv}\chi^2(\nu)\, (Inversechisquared distribution)

The chisquared distribution is a special case of type 3 Pearson distribution

If X \sim \chi^2(\nu_1)\, and Y \sim \chi^2(\nu_2)\, are independent then \tfrac{X}{X+Y} \sim {\rm Beta}(\tfrac{\nu_1}{2}, \tfrac{\nu_2}{2})\, (beta distribution)

If X \sim {\rm U}(0,1)\, (uniform distribution) then 2\log{(X)} \sim \chi^2(2)\,

\chi^2(6)\, is a transformation of Laplace distribution

If X_i \sim \mathrm{Laplace}(\mu,\beta)\, then \sum_{i=1}^n{\frac{2 X_i\mu}{\beta}} \sim \chi^2(2n)\,

chisquared distribution is a transformation of Pareto distribution

Student's tdistribution is a transformation of chisquared distribution

Student's tdistribution can be obtained from chisquared distribution and normal distribution

Noncentral beta distribution can be obtained as a transformation of chisquared distribution and Noncentral chisquared distribution

Noncentral tdistribution can be obtained from normal distribution and chisquared distribution
A chisquared 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 kdimensional Gaussian random vector with mean vector μ and rank k covariance matrix C, then X = (Y−μ)^{T}C^{−1}(Y−μ) is chisquared distributed with k degrees of freedom.
The sum of squares of statistically independent unitvariance Gaussian variables which do not have mean zero yields a generalization of the chisquared distribution called the noncentral chisquared 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 Y^{T}AY is chisquared distributed with k−n degrees of freedom.
The chisquared distribution is also naturally related to other distributions arising from the Gaussian. In particular,

Y is Fdistributed, Y ~ F(k_{1},k_{2}) if \scriptstyle Y = \frac{X_1 / k_1}{X_2 / k_2} where X_{1} ~ χ²(k_{1}) and X_{2} ~ χ²(k_{2}) are statistically independent.

If X is chisquared distributed, then \scriptstyle\sqrt{X} is chi distributed.

If X_{1} ~ χ^{2}_{k1} and X_{2} ~ χ^{2}_{k2} are statistically independent, then X_{1} + X_{2} ~ χ^{2}_{k1+k2}. If X_{1} and X_{2} are not independent, then X_{1} + X_{2} is not chisquared distributed.
Generalizations
The chisquared distribution is obtained as the sum of the squares of k independent, zeromean, unitvariance 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_iX_i is not known. It may be, however, calculated using the property of characteristic functions of the chisquared random variable.^{[16]}
Chisquared distributions
Noncentral chisquared distribution
The noncentral chisquared distribution is obtained from the sum of the squares of independent Gaussian random variables having unit variance and nonzero means.
Generalized chisquared distribution
The generalized chisquared distribution is obtained from the quadratic form z′Az where z is a zeromean Gaussian vector having an arbitrary covariance matrix, and A is an arbitrary matrix.
Gamma, exponential, and related distributions
The chisquared 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 chisquared distribution has numerous applications in inferential statistics, for instance in chisquared 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 tdistribution. It enters all analysis of variance problems via its role in the Fdistribution, which is the distribution of the ratio of two independent chisquared random variables, each divided by their respective degrees of freedom.
Following are some of the most common situations in which the chisquared distribution arises from a Gaussiandistributed sample.

if X_{1}, ..., X_{n} are i.i.d. N(μ, σ^{2}) random variables, then \sum_{i=1}^n(X_i  \bar X)^2 \sim \sigma^2 \chi^2_{n1} where \bar X = \frac{1}{n} \sum_{i=1}^n X_i.

The box below shows some statistics based on X_{i} ∼ Normal(μ_{i}, σ^{2}_{i}), i = 1, ⋯, k, independent random variables that have probability distributions related to the chisquared distribution:
The chisquared distribution is also often encountered in Magnetic Resonance Imaging .^{[17]}
Table of χ^{2} value vs pvalue
The pvalue is the probability of observing a test statistic at least as extreme in a chisquared 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 pvalue. The table below gives a number of pvalues matching to χ^{2} for the first 10 degrees of freedom.
A low pvalue indicates greater statistical significance, i.e. greater confidence that the observed deviation from the null hypothesis is significant. A pvalue of 0.05 is often used as a cutoff between significant and notsignificant results.
Degrees of freedom (df)

χ^{2} value^{[18]}

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

History and name
This distribution was first described by the German statistician Friedrich Robert Helmert in papers of 18756,^{[20]} where he computed the sampling distribution of the sample variance of a normal population. Thus in German this was traditionally known as the Helmert'sche ("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 chisquared test, published in 1900, with computed table of values published in (Elderton 1902), collected in (Pearson 1914, pp. xxxi–xxxiii, 26–28, Table XII). The name "chisquared" 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 ½x^{T}Σ^{−1}x (Σ being the covariance matrix).^{[21]} The idea of a family of "chisquared distributions", however, is not due to Pearson but arose as a further development due to Fisher in the 1920s.
See also
References

^ M.A. Sanders. "Characteristic function of the central chisquared distribution" (PDF). Retrieved 20090306.

^ Abramowitz, Milton; Stegun, Irene A., eds. (1965), "Chapter 26", .

^ NIST (2006). Engineering Statistics Handbook  ChiSquared Distribution

^ Jonhson, N. L.; Kotz, S.; Balakrishnan, N. (1994). "ChiSquared Distributions including Chi and Rayleigh". Continuous Univariate Distributions 1 (Second ed.). John Willey and Sons. pp. 415–493.

^ Mood, Alexander; Graybill, Franklin A.; Boes, Duane C. (1974). Introduction to the Theory of Statistics (Third ed.). McGrawHill. pp. 241–246.

^ Westfall, Peter H. (2013). Understanding Advanced Statistical Methods. Boca Raton, FL: CRC Press.

^ Ramsey, PH (1988). "Evaluating the Normal Approximation to the Binomial Test". Journal of Educational Statistics 13 (2): 173–82.

^ ^{a} ^{b} Lancaster, H.O. (1969), The Chisquared Distribution, Wiley

^ Dasgupta, Sanjoy D. A.; Gupta, Anupam K. (2002). "An Elementary Proof of a Theorem of Johnson and Lindenstrauss" (PDF). Random Structures and Algorithms 22: 60–65.

^ Chisquared distribution, from MathWorld, retrieved Feb. 11, 2009

^ M. K. Simon, Probability Distributions Involving Gaussian Random Variables, New York: Springer, 2002, eq. (2.35), ISBN 9780387346571

^ Box, Hunter and Hunter (1978). Statistics for experimenters. Wiley. p. 118.

^ Bartlett, M. S.; Kendall, D. G. (1946). "The Statistical Analysis of VarianceHeterogeneity and the Logarithmic Transformation". Supplement to the Journal of the Royal Statistical Society 8 (1): 128–138.

^ Shoemaker, Lewis H. (2003). "Fixing the F Test for Equal Variances".

^ Wilson, E. B.; Hilferty, M. M. (1931). "The distribution of chisquared" (PDF).

^ Davies, R.B. (1980). "Algorithm AS155: The Distributions of a Linear Combination of χ^{2} Random Variables". Journal of the Royal Statistical Society 29 (3): 323–333.

^ den Dekker A. J., Sijbers J., (2014) "Data distributions in magnetic resonance images: a review", Physica Medica, [2]

^ ChiSquared Test Table B.2. Dr. Jacqueline S. McLaughlin at The Pennsylvania State University. In turn citing: R.A. Fisher and F. Yates, Statistical Tables for Biological Agricultural and Medical Research, 6th ed., Table IV

^ F. R. Helmert, "Ueber die Wahrscheinlichkeit der Potenzsummen der Beobachtungsfehler und über einige damit im Zusammenhange stehende Fragen", Zeitschrift für Mathematik und Physik 21, 1876, S. 102–219

^ R. L. Plackett, Karl Pearson and the ChiSquared Test, International Statistical Review, 1983, 61f. See also Jeff Miller, Earliest Known Uses of Some of the Words of Mathematics.
Further reading
External links

Hazewinkel, Michiel, ed. (2001), "Chisquared distribution",

Calculator for the pdf, cdf and quantiles of the chisquared distribution

Earliest Uses of Some of the Words of Mathematics: entry on Chi squared has a brief history

Course notes on ChiSquared Goodness of Fit Testing from Yale University Stats 101 class.

², for a normal populationx demonstration showing the chisquared sampling distribution of various statistics, e.g. ΣMathematica

Simple algorithm for approximating cdf and inverse cdf for the chisquared distribution with a pocket calculator














Mixed continuousdiscrete univariate distributions













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