Usually, we have two variables.
通常我们有两个变量。
Oh, a function of two variables.
这的确是一个二元函数。
Here, you have two variables of integration.
这边需要对两个变量做积分。
Both methods work with more than two variables.
超过两个变量，这两个方法都能用。
The next two variables are the replacement for it.
接下来的两个变量就是用来替代它的。
First we create two variables.
首先我们建立两个变量。
The last two variables are bound to the edit boxes.
最后两个变量绑定到编辑框。
In step B2, there are two variables: E-mail and password.
在步骤B2中，这里有两个变量：电子邮件地址和密码。
Well, let's say how to visualize a function of two variables.
如何想象一个有两个变量的函数。
Simple correlation analysis was adopted for two variables.
两个变量之间的相关性用简单相关分析。
Covariance is a measure of how much two variables move together.
协方差衡量的是两个变量一起变动的情况。
Of course, these two variables are typically related in practice.
当然，这两个变量在实际情况下通常是相关的。
So, there is another way to plot the functions of two variables.
还有另外的一种方法画出二元函数的图像。
That method works exactly the same way as it did in two variables.
在两个变量的情形中，这种方法同样奏效。
It is a surface integral, so we need to have two variables in there.
它是一个面积分，所以这里需要两个变量。
That was our first example of a function of two variables possibly.
这是二元函数中的第一个例子。
So, we are going to focus on the case of functions of two variables.
我们将把注意力集中在二元函数上。
OK, so now, let's say that we had, actually, a function of two variables.
可能是这样的，现在我们可以说，有一个含有两个变量的函数。
So, when we think of a graph, really, it is a function of two variables.
因此当我们在想像一个图像时，其实那只是一个二元函数的图像。
Limit of function of two variables is more complex than that of one variable.
二元函数的极限远比一元函数的极限复杂，但它们之间又有密切的联系。
At the same time, it tries to explore the relationship between the two variables.
同时，本文还要探索这两个变量之间是否存在关系。
At the same time, it tries to explore the relationship between the two variables.
同时，本文还要探索这两个变量之间是否存在关系。
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