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Welch–Satterthwaite equation

For n sample variances s_i² (i = 1, ..., n), each respectively having ν_i degrees of freedom, often one computes the linear combination.

${\displaystyle \chi '=\sum _{i=1}^{n}k_{i}s_{i}^{2}.}$

where ${\displaystyle k_{i}}$ is a real positive number, typically ${\displaystyle k_{i}={\frac {1}{\nu _{i}+1}}}$. In general, the probability distributionofχ' cannot be expressed analytically. However, its distribution can be approximated by another chi-squared distribution, whose effective degrees of freedom are given by the Welch–Satterthwaite equation

${\displaystyle \nu _{\chi '}\approx {\frac {\displaystyle \left(\sum _{i=1}^{n}k_{i}s_{i}^{2}\right)^{2}}{\displaystyle \sum _{i=1}^{n}{\frac {(k_{i}s_{i}^{2})^{2}}{\nu _{i}}}}}}$

There is no assumption that the underlying population variances σ_i² are equal. This is known as the Behrens–Fisher problem.

The result can be used to perform approximate statistical inference tests. The simplest application of this equation is in performing Welch's t-test.

See also[edit]

• Pooled variance

References[edit]

Further reading[edit]

• Satterthwaite, F. E. (1946), "An Approximate Distribution of Estimates of Variance Components.", Biometrics Bulletin, 2 (6): 110–114, doi:10.2307/3002019, JSTOR 3002019, PMID 20287815
• Welch, B. L. (1947), "The generalization of "student's" problem when several different population variances are involved.", Biometrika, 34 (1/2): 28–35, doi:10.2307/2332510, JSTOR 2332510, PMID 20287819
• Neter, John; John Neter; William Wasserman; Michael H. Kutner (1990). Applied Linear Statistical Models. Richard D. Irwin, Inc. ISBN 0-256-08338-X.
• Michael Allwood (2008) "The Satterthwaite Formula for Degrees of Freedom in the Two-Sample t-Test", AP Statistics, Advanced Placement Program, The College Board. [1]