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Similar to the noncentral χ2 distribution, the toolbox calculates noncentral F distribution probabilities as a weighted sum of incomplete beta functions using Poisson probabilities as the weights.
I(x|a,b) is the incomplete beta function with parameters a and b, and δ is the noncentrality parameter.
As with the χ2 distribution, the F distribution is a special case of the noncentral F distribution. The F distribution is the result of taking the ratio of χ2 random variables each divided by its degrees of freedom.
If the numerator of the ratio is a noncentral chi-square random variable divided by its degrees of freedom, the resulting distribution is the noncentral F distribution.
The main application of the noncentral F distribution is to calculate the power of a hypothesis test relative to a particular alternative.