Power Regression
It analyzes the entered distribution table using power regression and plots a graph.
Power Regression: y = AxB
Interpreting the Correlation Coefficient r
0.7 < |r| ≤ 1 Strong correlation
0.4 < |r| < 0.7 Moderate correlation
0.2 < |r| < 0.4 Weak correlation
0 ≤ |r| < 0.2 No correlation0-1:\normalsize\ Power\ regression\\
(1)\ mean:\ \overline{\ln x}={\large \frac{{\small \sum}{\ln x_i}}{n}},\hspace{10px}\overline{\ln y}={\large \frac{{\small \sum}{\ln y_i}}{n}}\\
(2)\ trend\ line:\ y=Ax^B,\hspace{10px} B={\normalsize \frac{Sxy}{Sxx}},\hspace{10px} A=\exp({\overline{\ln y}-B\overline{\ln x}})\\
\\
(3)\ correlation\ coefficient:\ r=\frac{\normalsize S_{xy}}{\normalsize \sqrt{S_{xx}}\sqrt{S_{yy}}}\\
\hspace{20px}S_{xx}={{\small \sum}(\ln x_i-\overline{\ln x})^2}={{\small \sum} \ln x_i^2}-n \cdot \overline{\ln x}^2\\
\hspace{20px}S_{yy}={{\small \sum}(\ln y_i-\overline{\ln y})^2}={{\small \sum} \ln y_i^2}-n \cdot \overline{\ln y}^2\\
\hspace{20
0.7 < |r| ≤ 1 Strong correlation
0.4 < |r| < 0.7 Moderate correlation
0.2 < |r| < 0.4 Weak correlation
0 ≤ |r| < 0.2 No correlation0-1:\normalsize\ Power\ regression\\
(1)\ mean:\ \overline{\ln x}={\large \frac{{\small \sum}{\ln x_i}}{n}},\hspace{10px}\overline{\ln y}={\large \frac{{\small \sum}{\ln y_i}}{n}}\\
(2)\ trend\ line:\ y=Ax^B,\hspace{10px} B={\normalsize \frac{Sxy}{Sxx}},\hspace{10px} A=\exp({\overline{\ln y}-B\overline{\ln x}})\\
\\
(3)\ correlation\ coefficient:\ r=\frac{\normalsize S_{xy}}{\normalsize \sqrt{S_{xx}}\sqrt{S_{yy}}}\\
\hspace{20px}S_{xx}={{\small \sum}(\ln x_i-\overline{\ln x})^2}={{\small \sum} \ln x_i^2}-n \cdot \overline{\ln x}^2\\
\hspace{20px}S_{yy}={{\small \sum}(\ln y_i-\overline{\ln y})^2}={{\small \sum} \ln y_i^2}-n \cdot \overline{\ln y}^2\\
\hspace{20
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