带度数的倒数回归
通过逆数回归分析输入的频率分布表，并绘制图表。
倒数回归：
| \(y=A+{\large \frac{B}{x}}\) |
相关系数r的解读
0.7＜|r|≦1 相关性强
0.4＜|r|＜0.7 相关性中等
0.2＜|r|＜0.4 相关性弱
0≦|r|＜0.2 无相关性0-1:\normalsize\ Inverse\ regression\\
(1)\ mean:\ \bar{x^{\tiny -1}}={\large \frac{{\small \sum}{x_i^{\tiny -1}}}{n}},\hspace{10px}\bar{y}={\large \frac{{\small \sum}{y_i}}{n}},\hspace{10px}n={\small \sum}f_i\\
(2)\ trend\ line:\ y=A+{\large \frac{B}{x}},\hspace{10px} B={\large\frac{Sxy}{Sxx}},\hspace{10px} A=\bar{y}-B\bar{x^{\tiny -1}}\\
\\
(3)\ correlation\ coefficient:\ r=\frac{\normalsize S_{xy}}{\normalsize \sqrt{S_{xx}}\sqrt{S_{yy}}}\\
\hspace{20px}S_{xx}={{\small \sum}(x_i^{\tiny -1}-\bar{x^{\tiny -1}})^2 f_i}={{\small \sum} (x_i^{\tiny -1})^2 f_i}-n \cdot \bar{x^{\tiny -1}}^2\\
\hspace{20px}S_{yy}={{\small \sum}(y_i-\bar{y})^2 f_i}={{\small \sum} y_i^2 f_i}-n \cdot \bar{y}^2\\
\hspace{20px}S_{xy}={{\small \sum}(x_i^{\t
0.7＜|r|≦1 相关性强
0.4＜|r|＜0.7 相关性中等
0.2＜|r|＜0.4 相关性弱
0≦|r|＜0.2 无相关性0-1:\normalsize\ Inverse\ regression\\
(1)\ mean:\ \bar{x^{\tiny -1}}={\large \frac{{\small \sum}{x_i^{\tiny -1}}}{n}},\hspace{10px}\bar{y}={\large \frac{{\small \sum}{y_i}}{n}},\hspace{10px}n={\small \sum}f_i\\
(2)\ trend\ line:\ y=A+{\large \frac{B}{x}},\hspace{10px} B={\large\frac{Sxy}{Sxx}},\hspace{10px} A=\bar{y}-B\bar{x^{\tiny -1}}\\
\\
(3)\ correlation\ coefficient:\ r=\frac{\normalsize S_{xy}}{\normalsize \sqrt{S_{xx}}\sqrt{S_{yy}}}\\
\hspace{20px}S_{xx}={{\small \sum}(x_i^{\tiny -1}-\bar{x^{\tiny -1}})^2 f_i}={{\small \sum} (x_i^{\tiny -1})^2 f_i}-n \cdot \bar{x^{\tiny -1}}^2\\
\hspace{20px}S_{yy}={{\small \sum}(y_i-\bar{y})^2 f_i}={{\small \sum} y_i^2 f_i}-n \cdot \bar{y}^2\\
\hspace{20px}S_{xy}={{\small \sum}(x_i^{\t
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