Now we don't know the entire covariance, we only have one sample here of this random variable.

现在不知道整个协方差，这里只有一个随机变量的样本。

It's called an eddy covariance tower.

它被称为涡流协方差塔。

And then we do the covariance between the vertical movements of wind and the concentration of gas.

然后计算风的垂直运动和气体浓度之间的协方差。

And the degree to which they do it together will tell you the magnitude of the covariance.

它共同行动的程度将揭协方差的大小。

Hopefully that gives you a little bit of intuition about what the covariance is trying to tell us.

希望这能让你对协方差试图告诉什么有一点直觉上的理解。

I want to connect to this definition of covariance to everything we've been doing with least squared regression.

想将协方差的这一概念一直在进行的最小二乘回归分析联系起来。

So this whole video, I'm just going to rewrite this definition of covariance right over here.

所以整个视频中，打算在这里重新书写协方差的这个定义。

Well, you could view this as the covariance of X with X. But we've actually already seen this.

好吧，你可以将这视为X自身的协方差。 但实际上已经见过这个概念了。

The covariance of a random variable with itself is really just the variance of that random variable.

一个随机变量自身的协方差实际上就是该随机变量的方差。

And if we kept doing this, let's say for the entire population this happened, then it would make sense that they have a negative covariance.

如果持续这样做，假设在整个总体中这种情况都发生， 那么它具有负协方差就合乎情理了。 当一个上升时， 另一个下降。

And really it's just kind of a fun math thing to do to show you all of these connections, and where, really, the definition of covariance really becomes useful.

实际上，这只是为了展所有这些联系，并指出协方差定义真正发挥作用的地方，而进行的一项有趣的数探索。

So we were installing what's called an " eddy covariance tower, " which is a series of instruments that measures the exchange of greenhouse gases, like carbon dioxide and methane, between the land and the atmosphere.

所以安装了一个被称为“涡度协方差塔”的装置，这种装置由多种仪器构成，用于测量地面大气温室气体的交换，例如二氧化碳和甲烷。

Gosselin: So on the tower for eddy covariance, in principle, there's two main instruments: one instrument that measures the concentration of gas that we're interested in in the air and [another] that uses infrared.

Gosselin：原则上，在涡流协方差塔上有两种主要仪器：一种测量空气中感兴趣的气体浓度，[另一种]使用红外线。

Now, this right here-- so everything we've learned right now-- this right here is the covariance, or this is an estimate of the covariance of X and Y. Now what is this over here?

现在这里——目前所的一切——这正是XY的协方差，或者说，这是对XY协方差的一个估计。那么，这里这个是什么呢？