# Research on the Influence of Tractor Parameters on Shift Quality, Based on Uniform Design
## 3 Simulation Analysis of the Shift Process
### Analysis on the Influence of Tractor Parameters on the Shift Quality (Range 1 to Range 2)
#### 3.4.3 The Influence of Combined Parameters on Acceptable Jerk  
Firstly, the multiple linear regression model was used for the analysis and processing of the test data. Secondly, the stepwise regression method was selected to select the variables, and then the variable with the greatest contribution was selected for inclusion in the regression equation. Two pre-determined thresholds, \(F_{in}\) and \(F_{out}\), were used to determine whether a variable should be selected or eliminated from the regression equation. Finally, the regression equation was as follows:  
\[J_{ajv}=-4.842+1.440F_{r} \tag{35}\]  
The value's residual standard deviation \(\left(\vartheta^{*}\right)\) and multi-correlation index \(\left(R^{2}\right)\) in the regression analysis were 2.741 and 0.916, respectively. Equation (35) shows that only one parameter, \(F_{r}\), had a significant effect on \(J_{ajv}\), while the other three parameters had no significant effect. This is not consistent with actual experience. Practical problems often  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & \(N_{c^{*}}\)**Engine Speed & \(F_{r}\): Tractive Resistance of Tractor & \(P_{c^{*}}\)**Clutch Oil Pressure & \(D_{oi}\): Office Diameter & **Observed Response** \\  & (\(f\)/min) & **(kN)** & **(MPa)** & **(mm)** & **Value y** \\ \hline
1 & 1000(1) & 8(2) & 1.4(3) & 3.75(6) & y1 \\
2 & 1200(2) & 14(4) & 2(6) & 3.5(5) & y2 \\
3 & 1400(3) & 20(6) & 1.2(2) & 3.25(4) & y3 \\
4 & 1600(4) & 5(1) & 1.8(5) & 3(3) & y4 \\
5 & 1800(5) & 11(3) & 1(1) & 2.75(2) & y5 \\
6 & 2000(6) & 17(5) & 1.6(4) & 2.5(1) & y6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design of shift test scheme influenced by multi-factor, \(U_{6}^{*}\left(6^{4}\right)\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & \(N_{c^{*}}\)**(\(\mathbf{r/min}\)) & \(F_{r^{*}}\)**(\(\mathbf{kN}\)) & \(P_{c^{*}}\)**(MPa)** & \(D_{o^{*}}\)**(mm) & \(J_{ajv}\)**:(\(ml/s^{3}\))** & \(t_{e^{*}}\) **(s)** \\ \hline
1 & 1000(1) & 8(2) & 1.4(3) & 3 need to consider high-order interaction, so the linear model does not necessarily meet the requirements. Therefore, a quadratic regression model was proposed, as follows:  
\[Y=\beta_{0}+\sum_{i=1}^{m}\beta_{i}X_{i}+\sum_{i=1}^{m}\beta_{ii}X_{i}^{2}+\sum _{i<j}\beta_{ij}X_{i}X_{j}+\varepsilon_{0} \tag{36}\]  
where \(\beta_{0}\), \(\beta_{i}\), \(\beta_{ii}\), \(\beta_{ij}\) are the regression coefficients; \(\varepsilon_{0}\) is the random error. There are 15 terms in Equation (36). The regression equation can be obtained by using a stepwise regression technique, as follows:  
\[J_{ajv}=9.969+0.054F_{r}\times F_{r}-0.14P_{c}\times D_{o} \tag{37}\]  
The value's residual standard deviation (\(\sigma^{*}\)) and multi-correlation index (\(R^{2}\)) in this regression analysis were 0.952 and 0.992, respectively, and \(t_{0}=\)5.918, \(t_{1}=\)17.336, \(t_{2}=\)\(-\)4.709. Obviously, the effect of Equation (37), was better than Equation (35).  
Equation (37) shows that: parameters, \(F_{r}\), and interaction, \(P_{c}\times D_{o}\), had significant effects on \(J_{ajv}\), because \(|t_{1}|>|t_{2}|\), so \(F_{r}\) was more important than \(P_{c}\times D_{o}\). The larger the tractive resistance of the tractor, \(F_{r}\), is, the more significant the impact on the acceptable jerk, \(J_{ajv}\), is. The clutch oil pressure, \(P_{c}\), and the orifice diameter, \(D_{o}\), had a negative interaction with the acceptable jerk, \(J_{ajv}\).