In statistics, Cochran's theorem, devised by William G. Cochran,[1] is a theorem used to justify results relating to the probability distributions of statistics that are used in the analysis of variance.[2]



Suppose U1, ..., UN are i.i.d. standard normally distributed random variables, and there exist matrices  , with  . Further suppose that  , where ri is the rank of  . If we write


so that the   are quadratic forms, then Cochran's theorem states that the Qi are independent, and each Qi has a chi-squared distribution with ri degrees of freedom.[1]

Less formally, it is the number of linear combinations included in the sum of squares defining Qi, provided that these linear combinations are linearly independent.


We first show that the matrices B(i) can be simultaneously diagonalized and that their non-zero eigenvalues are all equal to +1. We then use the vector basis that diagonalize them to simplify their characteristic function and show their independence and distribution.[3]

Each of the matrices B(i) has rank ri and thus ri non-zero eigenvalues. For each i, the sum   has at most rank  . Since  , it follows that C(i) has exactly rank N − ri.

Therefore B(i) and C(i) can be simultaneously diagonalized. This can be shown by first diagonalizing B(i). In this basis, it is of the form:


Thus the lower   rows are zero. Since  , it follows that these rows in C(i) in this basis contain a right block which is a   unit matrix, with zeros in the rest of these rows. But since C(i) has rank N − ri, it must be zero elsewhere. Thus it is diagonal in this basis as well. It follows that all the non-zero eigenvalues of both B(i) and C(i) are +1. Moreover, the above analysis can be repeated in the diagonal basis for  . In this basis   is the identity of an   vector space, so it follows that both B(2) and   are simultaneously diagonalizable in this vector space (and hence also together B(1)). By iteration it follows that all B-s are simultaneously diagonalizable.

Thus there exists an orthogonal matrix   such that for all  ,   is diagonal, where any entry   is equal to 1 for   and is equal to 0 for any other indices.

Let   denote some specific linear combination of all   after transformation by  . Note that   due to the length preservation of the orthogonal matrix S.

The characteristic function of Qi is:


This is the Fourier transform of the chi-squared distribution with ri degrees of freedom. Therefore this is the distribution of Qi.

Moreover, the characteristic function of the joint distribution of all the Qis is:


From this it follows that all the Qis are independent.


Sample mean and sample varianceEdit

If X1, ..., Xn are independent normally distributed random variables with mean μ and standard deviation σ then


is standard normal for each i. It is possible to write


(here   is the sample mean). To see this identity, multiply throughout by   and note that


and expand to give


The third term is zero because it is equal to a constant times


and the second term has just n identical terms added together. Thus


and hence


Now the rank of Q2 is just 1 (it is the square of just one linear combination of the standard normal variables). The rank of Q1 can be shown to be n − 1, and thus the conditions for Cochran's theorem are met.

Cochran's theorem then states that Q1 and Q2 are independent, with chi-squared distributions with n − 1 and 1 degree of freedom respectively. This shows that the sample mean and sample variance are independent. This can also be shown by Basu's theorem, and in fact this property characterizes the normal distribution – for no other distribution are the sample mean and sample variance independent.[4]


The result for the distributions is written symbolically as


Both these random variables are proportional to the true but unknown variance σ2. Thus their ratio does not depend on σ2 and, because they are statistically independent. The distribution of their ratio is given by


where F1,n − 1 is the F-distribution with 1 and n − 1 degrees of freedom (see also Student's t-distribution). The final step here is effectively the definition of a random variable having the F-distribution.

Estimation of varianceEdit

To estimate the variance σ2, one estimator that is sometimes used is the maximum likelihood estimator of the variance of a normal distribution


Cochran's theorem shows that


and the properties of the chi-squared distribution show that


Alternative formulationEdit

The following version is often seen when considering linear regression.[5] Suppose that   is a standard multivariate normal random vector (here   denotes the n-by-n identity matrix), and if   are all n-by-n symmetric matrices with  . Then, on defining  , any one of the following conditions implies the other two:

  •   (thus the   are positive semidefinite)
  •   is independent of   for  

See alsoEdit


  1. ^ a b Cochran, W. G. (April 1934). "The distribution of quadratic forms in a normal system, with applications to the analysis of covariance". Mathematical Proceedings of the Cambridge Philosophical Society. 30 (2): 178–191. doi:10.1017/S0305004100016595.
  2. ^ Bapat, R. B. (2000). Linear Algebra and Linear Models (Second ed.). Springer. ISBN 978-0-387-98871-9.
  3. ^ Craig A.T. (1938) On The Independence of Certain Estimates of Variances. Ann. Math. Statist. 9, pp. 48–55
  4. ^ Geary, R.C. (1936). "The Distribution of the "Student's" Ratio for the Non-Normal Samples". Supplement to the Journal of the Royal Statistical Society. 3 (2): 178–184. doi:10.2307/2983669. JFM 63.1090.03. JSTOR 2983669.
  5. ^ "Cochran's Theorem (A quick tutorial)" (PDF).