MATLAB-对数据重新排序以显示钟形的智能方法？

我有一个810倍的向量，它有一个很好的理由（不相关，所以这里省略了原因）大致遵循高斯分布。我现在要核实这一点

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1917.29156813389

由于向量没有顺序，直接按当前顺序绘制它们会产生一个混乱的图，如图所示

什么是方便地重新排序数据的智能方法，以便我可以验证它们是否大致遵循正态分布？

只需使用以下函数：

histogram(X)

---

要查看PDF，您可以使用：

if exist('histogram','func')
  histogram(data)
else
  hist(data)
end

我觉得这很正常。你可以尝试用高斯函数来建模

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如果您有统计工具箱，您还可以运行一个假设测试，如，它将告诉您（在置信限内）您的点从正态分布得出的可能性有多大

[h, p] = jbtest(X, 0.05);
if h
    disp(sprintf('There is less than %.3f%% chance that your data is abnormal', 100 * p));
else
    disp(sprintf('Your data is not normal within your confidence limit'));
end

这不会给你一个漂亮的情节，但它会给你一个不那么偏颇的答案，而不仅仅是盯着它看

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在这种情况下，您的数据集有99.9%以上的可能性来自正态分布。

要查看您的数据是否遵循高斯分布，您可以使用。它在一定尺度上绘制数据的经验分布函数，使得高斯对应于一条直线。直线越直，越像高斯曲线

[h, p] = jbtest(X, 0.05);
if h
    disp(sprintf('There is less than %.3f%% chance that your data is abnormal', 100 * p));
else
    disp(sprintf('Your data is not normal within your confidence limit'));
end

如果经常发生这样的情况，曲线的中心部分是直的，并且在其末端有偏差。你的数据就是这样；见下图

解释是，高斯分布通常是由随机变量之和产生的，这是由于。当求和数趋于无穷大时，求和的分布在极限范围内为高斯分布。对于有限数量的项，分布仅近似为高斯分布；在中心的近似比在尾部的好（事实上，这就是为什么这个定理被称为中心）

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你不是在找直方图吗？@HighPerformanceMark噢，天哪。我真傻。。。谢谢！那么我应该删除这个问题吗？facepalm@FarticlePilter删除这个问题可能是合适的……如果我发布了这个问题，我会删除它，但这真的取决于你，它不会比网上75%的问题更糟。@HighPerformanceMark太迟了。。。因为它已经有了答案，我自己不能删除它。不过，我已将其标记出来，留给版主决定。：）是的，但是

hist

已经被弃用，取而代之的是

histogram

。histogram是一个新函数。我仍然在R2012上，所以感谢您的更新！答案已附加