In statistics, Cochran's theorem is used in the analysis of variance.
Suppose U1, ..., Un are independent standard normally distributed random variables, and an identity of the form
can be written where each Qi is a sum of squares of linear combinations of the Us. Further suppose that
where ri is the rank of Qi. Cochran's theorem states that the Qi are independent, and Qi has a chi-square distribution with ri degrees of freedom.
Cochran's theorem is the converse of Fisher's theorem.
If X1, ..., Xn are independent normally distributed random variables with mean μ and standard deviation σ
is standard normal for each i.
It is possible to write
(here, summation is from 1 to n, that is over the observations).
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 is just n identical terms added together.
Combining the above results (and dividing by σ2), we have:
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 distribution with n − 1 and 1 degree of freedom respectively.
This shows that the sample mean and sample variance are independent; also
To estimate the variance σ2, one estimator that is often used is
Cochran's theorem shows that
which shows that the expected value of is σ2(n − 1)/n.
Both these distributions are proportional to the true but unknown variance σ2; thus their ratio is independent of σ2 and because they are independent we have
where F1,n − 1 is the F-distribution with 1 and n − 1 degrees of freedom (see also Student's t-distribution).
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