平均值,StDev超过65536个元素?

时间:2013-02-24 22:06:10

标签: excel-vba average vba excel

我正在尝试计算(在VBA Excel中)超过65536个元素的数组的Average和StDev。像这样:

Mitja = worksheetfunction.Average(array()) DesvTip = worksheetfunction.StDev(array())

虽然阵列的尺寸小于65536但没有问题,但是当它更大时会给我一个错误!

我知道这个VBA函数不能使用超过65536个数据,所以如何在VBA中获取这些参数?

Apreciate您的意见。非常感谢! :))

3 个答案:

答案 0 :(得分:1)

您可以计算平均值和标准差,而无需存储所有值。只需保持总和,平方和和点数。你可以拥有与整数个点数一样多的点。

以下是我在Java中的表现。随意小儿床。

package statistics;

/**
 * Statistics
 * @author Michael
 * @link http://stackoverflow.com/questions/11978667/online-algorithm-for-calculating-standrd-deviation/11978689#11978689
 * @link http://mathworld.wolfram.com/Variance.html
 * @since 8/15/12 7:34 PM
 */
public class Statistics {

    private int n;
    private double sum;
    private double sumsq;

    public void reset() {
        this.n = 0;
        this.sum = 0.0;
        this.sumsq = 0.0;
    }

    public synchronized void addValue(double x) {
        ++this.n;
        this.sum += x;
        this.sumsq += x*x;
    }

    public synchronized double calculateMean() {
        double mean = 0.0;
        if (this.n > 0) {
            mean = this.sum/this.n;
        }
        return mean;
    }

    public synchronized double calculateVariance() {
        double variance = 0.0;
        if (this.n > 0) {
            variance = Math.sqrt(this.sumsq-this.sum*this.sum/this.n)/this.n;
        }
        return variance;
    }

    public synchronized double calculateStandardDeviation() {
        double deviation = 0.0;
        if (this.n > 1) {
            deviation = Math.sqrt((this.sumsq-this.sum*this.sum/this.n)/(this.n-1));
        }
        return deviation;
    }
}

答案 1 :(得分:0)

如果数据存储在数组x(1 to N, 1 to 1)中,则使用以下算法,其中N是数据点的数量

sum = 0# : sumsq = 0#
for i=1 to N
  sum = sum + x(i,1)
  sumsq = sumsq + x(i,1)^2
next i

average = sum/N
stddev = Sqr( sumsq/N^2 - sum^2/N^3 )

:注意: 要填充数组,请使用符号

Dim r as Range, x() as Variant
Set r = Range("A1").Resize(N,1)
x = r.Value

答案 2 :(得分:0)

感谢您的评论。最后我们做了类似的事情。我希望它对有同样问题的人有用。我们的代码:

sum = 0
sumq = 0

For i = 0 To ((2 * N) - 1)
    sum = sum + h_normal(i)
Next i

media = sum / (2 * N)

For j = 0 To ((2 * N) - 1)
    sumsq = sumsq + (h_normal(j) - media) ^ 2
Next j

desviaci(h - 1) = Math.Sqr(sumsq / ((2 * N) - 1))
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