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In probability theory and statistics, the noncentral F-distribution is a continuous probability distribution that is a generalization of the (ordinary) F-distribution. It describes the distribution of the quotient (X/n1)/(Y/n2), where the numerator X has a noncentral chi-squared distribution with n1 degrees of freedom and the denominator Y has a central chi-squared distribution with n2 degrees of freedom. It is also required that X and Y are statistically independent of each other.

It is the distribution of the test statistic in analysis of variance problems when the null hypothesis is false. The noncentral F-distribution is used to find the power function of such a test.


Occurrence and specificationEdit

If   is a noncentral chi-squared random variable with noncentrality parameter   and   degrees of freedom, and   is a chi-squared random variable with   degrees of freedom that is statistically independent of  , then


is a noncentral F-distributed random variable. The probability density function (pdf) for the noncentral F-distribution is[1]


when   and zero otherwise. The degrees of freedom   and   are positive. The term   is the beta function, where


The cumulative distribution function for the noncentral F-distribution is


where   is the regularized incomplete beta function.

The mean and variance of the noncentral F-distribution are




Special casesEdit

When λ = 0, the noncentral F-distribution becomes the F-distribution.

Related distributionsEdit

Z has a noncentral chi-squared distribution if


where F has a noncentral F-distribution.

See also noncentral t-distribution.


The noncentral F-distribution is implemented in the R language (e.g., pf function), in MATLAB (ncfcdf, ncfinv, ncfpdf, ncfrnd and ncfstat functions in the statistics toolbox) in Mathematica (NoncentralFRatioDistribution function), in NumPy (random.noncentral_f), and in Boost C++ Libraries.[2]

A collaborative wiki page implements an interactive online calculator, programmed in the R language, for the noncentral t, chi-squared, and F distributions, at the Institute of Statistics and Econometrics, School of Business and Economics, Humboldt-Universität zu Berlin.[3]


  1. ^ S. Kay, Fundamentals of Statistical Signal Processing: Detection Theory, (New Jersey: Prentice Hall, 1998), p. 29.
  2. ^ John Maddock, Paul A. Bristow, Hubert Holin, Xiaogang Zhang, Bruno Lalande, Johan Råde. "Noncentral F Distribution: Boost 1.39.0". Retrieved 20 August 2011.CS1 maint: Uses authors parameter (link)
  3. ^ Sigbert Klinke (10 December 2008). "Comparison of noncentral and central distributions". Humboldt-Universität zu Berlin.