Examples of org.apache.commons.math3.distribution.FDistribution.cumulativeProbability()

        System.out.println(bn.cumulativeProbability(32));
        System.out.println(n.cumulativeProbability(32));
        System.out.println(normal.density(p, param_norm));


        p.array[0] = 27;
        System.out.println(bn.cumulativeProbability(27));
        System.out.println(n.cumulativeProbability(27));
        System.out.println(n.density(27));
        System.out.println(normal.density(p, param_norm));


        p.array[0] = 60;

        System.out.println(n.cumulativeProbability(27));
        System.out.println(n.density(27));
        System.out.println(normal.density(p, param_norm));


        p.array[0] = 60;
        System.out.println(bn.cumulativeProbability(60));
        System.out.println(n.cumulativeProbability(60));
        System.out.println(n.density(60));
        System.out.println(normal.density(p, param_norm));

        DimensionMismatchException, MaxCountExceededException {


        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final ChiSquaredDistribution distribution =
            new ChiSquaredDistribution(null, expected.length - 1.0);
        return 1.0 - distribution.cumulativeProbability(chiSquare(expected, observed));
    }


    /**
     * Performs a <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
     * Chi-square goodness of fit test</a> evaluating the null hypothesis that the


        checkArray(counts);
        double df = ((double) counts.length -1) * ((double) counts[0].length - 1);
        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final ChiSquaredDistribution distribution = new ChiSquaredDistribution(df);
        return 1 - distribution.cumulativeProbability(chiSquare(counts));


    }


    /**
     * Performs a <a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc45.htm">

        MaxCountExceededException {


        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final ChiSquaredDistribution distribution =
                new ChiSquaredDistribution(null, (double) observed1.length - 1);
        return 1 - distribution.cumulativeProbability(
                chiSquareDataSetsComparison(observed1, observed2));


    }


    /**

            DimensionMismatchException, MaxCountExceededException {


        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final ChiSquaredDistribution distribution =
                new ChiSquaredDistribution(null, expected.length - 1.0);
        return 1.0 - distribution.cumulativeProbability(g(expected, observed));
    }


    /**
     * Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described
     * in p64-69 of McDonald, J.H. 2009. Handbook of Biological Statistics

            DimensionMismatchException, MaxCountExceededException {


        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final ChiSquaredDistribution distribution =
                new ChiSquaredDistribution(null, expected.length - 2.0);
        return 1.0 - distribution.cumulativeProbability(g(expected, observed));
    }


    /**
     * Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit
     * evaluating the null hypothesis that the observed counts conform to the

            MaxCountExceededException {


        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final ChiSquaredDistribution distribution =
                new ChiSquaredDistribution(null, (double) observed1.length - 1);
        return 1 - distribution.cumulativeProbability(
                gDataSetsComparison(observed1, observed2));
    }


    /**
     * <p>Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned

        throws NotPositiveException, NotStrictlyPositiveException,
        DimensionMismatchException, MaxCountExceededException {


        ChiSquaredDistribution distribution =
            new ChiSquaredDistribution(expected.length - 1.0);
        return 1.0 - distribution.cumulativeProbability(chiSquare(expected, observed));


    }


    /**
     * Performs a <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">


        checkArray(counts);
        double df = ((double) counts.length -1) * ((double) counts[0].length - 1);
        ChiSquaredDistribution distribution;
        distribution = new ChiSquaredDistribution(df);
        return 1 - distribution.cumulativeProbability(chiSquare(counts));


    }


    /**
     * Performs a <a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc45.htm">

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