# Design and Experiment of Grain Litter for Sorghum Harvester
## 4 Field Experiment and Analysis
### Orthogonal Test Results and Analysis
#### 4.4.2 Analysis of Variance  
In order to further optimize the test results, the quadratic regression analysis of variance and significance tests were carried out by Design-Expert 8.0.6.1 software, as shown in Table 4. After multiple regression fitting, the regression equations of ear loss and missing loss among each factor and assessment index were obtained:  
\[ \begin{split} Y_{1}=34.006+1.790X_{1}-0.454X_{2}-0.213X_{3}+8.06 \times 10^{-3}X_{1}X_{2}-3.61\times 10^{-3}X_{1}X_{3}+2.44\times 10^{-4}X_{2}X_{3}+ 0.115X_{1}^{2}+\\ 4.12\times 10^{-3}X_{2}^{2}+4.78\times 10^{-4}X_{3}^{2}\end{split}  \tag{21}\]  
\[ \begin{split} Y_{2}=41.985+0.644X_{1}-0.439X_{2}-0.281X_{3}+1.2 \times 10^{-2}X_{1}X_{2}+1.39\times 10^{-4}X_{1}X_{3}+2.61\times 10^{-4}X_{2}X_{3}+ 0.292X_{1}^{2}+\\ 3.76\times 10^{-3}X_{2}^{2}+6.17\times 10^{-4}X_{3}^{2}\end{split}  \tag{22}\] \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sources**} & \multicolumn{8}{c}{**Loss Rate of Ear**} & \multicolumn{8}{c}{**Loss Rate of Missing Collection**} \\ \cline{2-11}  & **Sum of** & **Degree of** & **Mean** & **F** & **\(p\)** & **Sum of** & **Degree of** & **Mean** & **F** & **\(p\)** \\  & **Squares** & **Freedom** & **Square** & **Value** & **Value** & **Squares** & **Freedom** & **Square** & **Value** & **Value** \\ \hline Model & 14.32 & 9 & 1.59 & 62.66 & \textless{}0.0001 ** & 18.93 & 9 & 2.1 & 20.98 & 0.0003 ** \\ \(X_{1}\) & 8.47 & 1 & 8.47 & 333.38 & \textless{}0.0001 ** & 11.86 & 1 & 11.86 & 118.25 & \textless{}0.0001 ** \\ \(X_{2}\) & 0.78 & 1 & 0.78 & 30.52 & 0.0009 ** & 1.58 & 1 & 1.58 & 15.8 & \textless{}0.0054 ** \\ \(X_{3}\) & 0.36 & 1 & 0.36 & 14.22 & 0.007 ** & 0.72 & 1 & 0.72 & 7.18 & 0.0316 * \\ \(X_{1}X_{2}\) & 0.021 & 1 & 0.021 & 0.83 & 0.3931 & 0.046 & 1 & 0.046 & 0.46 & 0.519 \\ \(X_{1}X_{3}\) & 0.017 & 1 & 0.017 & 0.67 & 0.4415 & 0.000025 & 1 & 0.000025 & 0.00025 & 0.9878 \\ \(X_{2}X_{3}\) & 0.048 & 1 & 0.048 & 1.91 & 0.2099 & 0.055 & 1 & 0.055 & 0.55 & 0.4822 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Variance analysis of quadratic response surface regression model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multicolumn{2}{c}{**Forward**} & \multirow{2}{*}{**Tilt Angle of Grain**} & \multirow{2}{*}{**Rotating Speed**} & \multirow{2}{*}{**Loss Rate of**} & \multirow{2}{*}{**Spoed X1**} & \multirow{2}{*}{**Lifer X2**} \\  & & & & & & \\ \hline
1 & 0 & 1 & \(-\)1 & 3.89 & 3.59 \\
2 & 0 & 0 & 0 & 2.73 & 2.86 \\
3 & 0 & \(-\)1 & \(-\)1 & 4.64 & 5.05 \\
4 & 1 & \(-\)1 & 0 & 4.99 & 5.16 \\
5 & 0 & 0 & 0 & 2.92 & 3.17 \\
6 & 0 & \(-\)1 & 1 & 3.92 & 4.46 \\
7 & 1 & 1 & 0 & 4.42 & 4.82 \\
8 & \(-\)1 & 0 & \(-\)1 & 2.26 & 2.69 \\
9 & \(-\)1 & 0 & 1 & 2.04 & 1.84 \\
10 & \(-\)1 & 1 & 0 & 2.12 & 2.01 \\
11 & \(-\)1 & \(-\)1 & 0 & 2.98 & 2.78 \\
12 & 1 & 0 & 1 & 3.87 & 4.12 \\
13 & 0 & 0 & 0 & 2.61 & 2.56 \\
14 & 0 & 1 & 1 & 3.61 & 3.47 \\
15 & 0 & 0 & 0 & 2.43 & 2.63 \\
16 & 0 & 0 & 0 & 2.61 & 2.49 \\
17 & 1 & 0 & \(-\)1 & 4.35 & 4.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test plan and results. According to Table 4, the significance test value of the regression model of ear loss \(p\) = 0.0003 (\(p\) < 0.01) indicates that the regression model of ear loss is extremely significant, the influence of primary \(X_{1}\), \(X_{2}\), \(X_{3}\) and secondary terms \(X_{2}\)\({}^{2}\), \(X_{3}\)\({}^{2}\) on the model was significant (\(p\) < 0.05), However, the quadratic terms \(X_{1}\)\({}^{2}\) and interaction terms \(X_{1}\)\(X_{2}\), \(X_{1}\)\(X_{3}\), \(X_{2}\)\(X_{3}\) and have no significant influence on the model, remove from the regression model. The significance test value of the leakage loss regression model \(p\) = 0.0003 (\(p\) < 0.01) indicates that the leakage loss regression model is extremely significant, the influence of primary \(X_{1}\), \(X_{2}\), \(X_{3}\) and secondary terms \(X_{2}\)\({}^{2}\), \(X_{3}\)\({}^{2}\) on the model was significant (\(p\) < 0.05).