Examples of org.apache.commons.math.stat.descriptive.moment.Variance

org.apache.commons.math.stat.descriptive.moment.Variance

cm.org/10.1145/359146.359152"> Chan, T. F. and J. G. Lewis 1979, Communications of the ACM, vol. 22 no. 9, pp. 526-531.

The evaluate methods leverage the fact that they have the full array of values in memory to execute a two-pass algorithm. Specifically, these methods use the "corrected two-pass algorithm" from Chan, Golub, Levesque, Algorithms for Computing the Sample Variance, American Statistician, vol. 37, no. 3 (1983) pp. 242-247.

increment

incrementAll

getResult

evaluate

The "population variance" ( sum((x_i - mean)^2) / n ) can also be computed using this statistic. The isBiasCorrected property determines whether the "population" or "sample" value is returned by the evaluate and getResult methods. To compute population variances, set this property to false.

Note that this implementation is not synchronized. If multiple threads access an instance of this class concurrently, and at least one of the threads invokes the increment() or clear() method, it must be synchronized externally.

        double[] values = new double[length];
        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


        Variance variance = new Variance(false);
        return variance.evaluate(values);
    }

        double[] values = new double[length];
        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


        Variance variance = new Variance();
        return variance.evaluate(values);
    }

        double[] values = new double[length];
        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


        Variance variance = new Variance(false);
        return variance.evaluate(values);
    }

        double[] values = new double[length];
        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


        Variance variance = new Variance();
        return variance.evaluate(values);
    }

     *
     * @return the variance 
     */
    public double getVariance() {
        if (varianceImpl == variance) {
            return new Variance(secondMoment).getResult();
        } else {
            return varianceImpl.getResult();
        }
    }

        double[] values = new double[length];
        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


        Variance variance = new Variance(false);
        return variance.evaluate(values);
    }

        double[] values = new double[length];
        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


        Variance variance = new Variance();
        return variance.evaluate(values);
    }

        double[] values = new double[length];
        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


        Variance variance = new Variance(false);
        return variance.evaluate(values);
    }

        double[] values = new double[length];
        for (int i = 0; i < length; i++) {
            values[i] = start + i;
        }


        Variance variance = new Variance();
        return variance.evaluate(values);
    }

Related Classes of org.apache.commons.math.stat.descriptive.moment.Variance

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