How do we think about how well our sample regression line fits our sample data?

我们怎样衡量我们的样本回归线拟合样本数据有多好呢？

youdao

Data from the regression line itself may be helpful to provide mathematical estimates of the degree of linearity.

来自回归线自身的数据可以用于提供线性程度的数学评估。

youdao

The correlation coefficient y-intercept slope of the regression line and residual sum of squares should be submitted.

应该提交其相关系数、Y轴截距、回归线斜率、残差平方和。

youdao

What Gauss did was said, let's fit a line through the point-- the scatter of points--and that's called the regression line.

高斯说,做这样的一条直线,切合所有散点,这就是回归直线

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That's called the regression line and the intercept is called alpha--there's alpha.

这就是回归直线,这个是截点,用alpha表示...这里是alpha

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The concept of regression goes back to the mathematician Gauss, who talked about fitting a line through a scatter of points.

回归这个概念要追溯到数学家高斯,讨论的是从若干散点中切合出一条直线

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R2 is most often seen as a number between 0 and 1, used to describe how well a regression line fits a set of data.

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He knows this because he has actual data on sales (available from Amazon) and the sales rank, and he knows how to plot a regression line in Excel.

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An R2 near 1 indicates that a regression line fits the data well, while an R2 close to 0 indicates a regression line does not fit the data very well.

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