This term right here is n times the mean of the y values.

此处的此项是 y 值平均值的 n 倍。

Or the central tendency for our y values.

或者我们的 y 值的集中趋势。

We calculated for each of the y values.

我们计算了每个 y 值。

Then the y value is going to be the mean of the xy's divided by the mean of the x's.

然后 y 值将是 xy 的平均值除以 x 的平均值。

It's figures out the squared distance of this y value from the mean.

它计算出此 y 值与平均值的平方距离。

And so what we calculated next was the total error, the squared error, from the means of our y values.

所以我们接下来计算的是总误差，平方误差，来自我们的 y 值的平均值。

The scanline algorithm starts by reading the three points that make up the polygon, and finding the lowest and highest Y values.

扫描线算法首先读取构成多边形的三个点，然后找到最低和最高 Y 值。

So the error is just going to be equal to our y value.

所以错误将等于我们的 y 值。

Well our line says, you give me an x value, I'm going to tell you what y value I'll predict.

好吧，我们的台词说，你给我一个 x 值，我将告诉你我将预测的 y 值。

So when x is equal to negative 2, the y value on the line is going to be the slope.

所以当 x 等于负 2 时，直线上的 y 值将成为斜率。

This term right over here is going to be minus 2bn times the mean of the y values.

此处的此项将是 y 值平均值的负 20 亿倍。

It's equal to y1 minus this y value.

它等于 y1 减去这个 y 值。

Or you can think of it as the y value of this point and the y value of the line.

或者你可以认为是这个点的y值和直线的y值。

How much of the total variation in the y values can be explained by the variation in the x values, or by the line itself?

y 值的总变化有多少可以用 x 值的变化或直线本身来解释？

Well what's this y value going to be?

那么这个 y 值是多少？

It's now figuring out the square distance from the line using what the line would predict and using the y value, this one.

它现在使用直线预测的值和 y 值计算出与直线的平方距离，这个。

This right here, that is the this y value right over here.

这就是这里，就是这里的这个 y 值。

This point's y value is over here.

这个点的y值就到这里了。

Now all of a sudden, this is the y value that my line would predict, it's now using this x value and sticking it over here.

现在突然间，这是我的线预测的 y 值， 它现在使用这个 x 值并将它贴在这里。

That's this value right over here, you take y1 minus the y value at this point in the line.

这就是这里的这个值，你用 y1 减去行中这一点的 y 值。