你是！ (You are!)

Actually not. This is not a text about you.

其实并不是。 这不是关于您的文字。

But, as Gladwell puts it in Outliers, if you find yourself being that type of outlier, you’re quite lucky. And rare.

但是，正如Gladwell在“ 离群值”中所说的那样，如果您发现自己属于这种离群值，那么您很幸运。 和罕见。

实际上是什么离群值？ (What is actually an outlier?)

According to Meriam-Webster, an outlier is:

根据Meriam-Webster的估计，离群值是：

“a statistical observation that is markedly different in value from the others of the sample”

“统计观察值与样本中其他值明显不同”

But you’re not here for that, are you?

但是，您不是在这里吗？

Let’s simply explain when a data point is considered an outlier, why that might happen, and what you can do about it.

让我们简单地解释一下何时将数据点视为异常值，为什么会发生这种异常以及您可以采取什么措施。

什么时候？ (When?)

There are multiple ways with which we can identify and highlight outliers but our goal here is to keep it short and simple, so let’s discuss the easiest way. You can find other ways here.

我们可以使用多种方法来识别和突出显示离群值，但是我们的目标是使其简短而简单，因此让我们讨论最简单的方法。 您可以在这里找到其他方法。

Any observed value is considered an outlier if it falls beyond the range of 1stQuartile-1.5 x IQR to 3rdQuartile + 1.5 x IQR.

如果任何观测值超出1stQuartile-1.5 x IQR到3rdQuartile + 1.5 x IQR的范围，则将其视为异常值。

Stay here!

留在这儿！

I promised it will be easy, so it will. We just have to fix what this IQR (inter-quartile-range) means.

我保证这会很容易，所以会。 我们只需要解决此IQR(四分位间距)的含义即可。

Let’s consider you’re meeting your highschool colleagues, 9 people. All coming in cars. For the purpose of this explanation, let’s image we collect data on the horsepower of all your cars in ascending order.

让我们考虑一下您正在与9位高中生见面。 都进来的车。 为了便于说明，让我们想象一下，我们以升序收集有关您所有汽车的马力的数据。

105 | 133 | 146 | 183 | 190 | 195 | 210 | 220 | 510 ← values collected

105 | 133 | 146 | 183 | 190 | 195 | 210 | 220 | 510←收集的值

Now if you know a bit of statistics, we have what is called quartiles. If you don’t remember please look here and then come back.

现在，如果您知道一些统计信息，我们就有所谓的四分位数。 如果您不记得了，请看这里然后再回来。

IQR = 3rdQuartile - 1stQuartile = 215–139.5 = 75.5

IQR =第三四分位数-1stQuartile = 215–139.5 = 75.5

Now, coming back to what is considered an outlier in our example, we need to calculate Q1-1.5 x IQR and Q3+1.5 x IQR.

现在，回到示例中被认为是异常值的地方，我们需要计算Q1-1.5 x IQR和Q3 + 1.5 x IQR。

• Q1 - 1.5 x IQR = 139.5–75.5 = 64 (Q1 — first quartile)

  Q1-1.5 x IQR = 139.5–75.5 = 64 ( Q1- 第一个四分位数)

• Q3 + 1.5 x IQR = 215 + 75.5 = 290.5 (Q3 — third quartile)

  Q3 + 1.5 x IQR = 215 + 75.5 = 290.5 (Q3-第三四分位数)

We’re very close. STAY HERE!

我们非常接近。 留在这里 ！

As mentioned before starting the calculation, any observed value that is outside the interval [64;290.5] is considered an outlier. An extreme value compared to the collected data. Question is, are there any values outside the interval in our data? That’s right, 510 is. (Let’s assume that’s you, you have a new BMW M5).

如开始计算之前所述，在间隔[64; 290.5]之外的任何观测值都被视为异常值。 与收集的数据相比的极值。 问题是，我们的数据间隔之外是否还有其他值？ 是的， 510是。 (假设您是您，您有新的BMW M5)。

And here we are, that is the very easy way of calculating outliers out of a set of simple collected data.

这就是从一组简单的收集数据中计算离群值的非常简单的方法。

为什么？ (Why?)

There are multiple reasons outliers might end up in a set of data. Both good and bad.

有多种原因可能导致离群值出现在一组数据中。 好与坏。

Data entry errors → instead of 510 you wanted to type 210 and thus the value became an outlier;

数据输入错误 →您想输入210而不是510，因此该值成为异常值；

Measurement errors → you’ve measured your car’s power at a service center that is well known for inflating the numbers. That 510 is not real;

测量误差 →您已经在服务中心测量了汽车的功率，该服务中心以数字夸大而闻名。 那510不是真实的；

Experimental errors → one of your colleagues, the one with 105 told you the value in kw not in horsepower, the misunderstanding is an experimental error;

实验错误→您的一位同事，有105个告诉您以kw表示的值而不是马力，误解是实验错误；

Intentional → you’re putting your colleagues to the test and tell them a value that is not real;

故意 →您正在对同事进行测试，并告诉他们一个不真实的价值；

Natural → and that is where we are, you’re really a hustler and your M5 power is not experimental measurement BS, you really are an outlier.

自然→这就是我们的位置，您真的是骗子，您的M5功率不是实验测量BS，您确实是一个异常值。

什么？ (What?)

Now that you know what they are, how you find them, and what may cause them, what can be done to make use or get rid of them?

现在，您知道它们是什么，如何找到它们以及可能导致它们的原因，可以采取哪些措施来利用或摆脱它们？

1. If you want to brag about how great the average of hp in your class is, keep the values. Consider that the average is not representative as it is influenced by the outlier. You.

   如果您要吹嘘班级中的平均功率是多少， 请保留这些值 。 考虑到平均值没有代表性，因为它受到异常值的影响。 您。

2. If you think your car is very different and you’re an exception to the other cars, take your value out.

   如果您认为自己的汽车与众不同，并且是其他汽车的例外，那么请充分利用自己的价值。

3. If you feel like there are other highschool colleagues with powerful cars but did not show up, make another meeting and treat your group as a different one.

   如果您觉得还有其他高中生有高功率汽车，但没有露面，请举行另一次会议并将您的小组视为另一小组 。

That was it.

就是这样

This is, as always, an oversimplistic and humoristic approach to explaining rather complex statistical concepts.

与往常一样，这是一种过于简单和幽默的方法，用于解释相当复杂的统计概念。

If you like my work, consider reading other posts of mine, I try to publish weekly:

如果您喜欢我的作品，请考虑阅读我的其他文章，我尝试每周发布一次：