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1·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？

2·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。

3·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。

4·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。

5·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。

6·If the observations used in the analysis extend back in time for several years, the resulting regression line may be too steep.

如果在分析中使用的观察值扩大到几年以前，那么推导出的回归直线就会可能太悬殊。

7·Calculate the regression line of test results versus analyte concentrations. The correlation coefficient should be more than 0.99.

以测定结果的响应信号作为被测物浓度的函数作图，相关系数应大于0.99。

8·The standard error of estimate, on the other hand, measures the variability, or scatter, of the observed values around the regression line.

而估计值的平均误差，却是度量观察值围绕着回归直线的变化程度或分散程度。

9·A new correlation coefficient that indicates goodness of-fit for regression line will be introduced yet in this paper. The illustrative examples are presented.

本文还提出一种反映回归直线拟合优度的新的相关系数。

10·Based on the fact that total error is needed by the system, this thesis emphasizes particularly on analyzing the static regression line error model and its engineering feasibility.

侧重分析静态回归直线误差模型及工程应用的可行性，通过例证比较说明统计估算法的优势。