## 4 Results
### Results of Analyses Investigating the Effects of Three Factors
#### 4.3.2 Analysis of the Triplet Camera Type, Rotation and Device Used  
The next triplet to be examined was the camera type, rotation, and device used. The camera rotation factor used the same groups as before. These rotation and contrast ratio groups were defined beforehand in Sections 4.1.3 and 4.1.4. Then, 8 groups were formed based on all possible levels of the variables.  
As before, the numerical values of average ratios and further statistical characteristics are presented in Table 16. Performing the logistic regression analysis, the authors obtained the results presented in Table 31.  
As can be seen, \"Orthographic, NO_R, Gear VR\" has some, but not a significant, decrease in the results compared to \"Orthographic, NO_R, desktop display\". The others are significantly better. The smallest improvement is in \"Perspective, NO_R, Gear VR\" and the greatest improvements are in \"Perspective, INC_R, desktop display\" and \"Perspective, INC_R, Gear VR\". The difference between the two best cases is not significant (\(p\)-value = 0.3012).  
When comparing the model that does not allow interactions (I), and the model that allows the interactions of two variables (II) and the model that allows the interactions of all variables (III), model II is significantly better than model I (\(p\)-value = 0.01175), and model III is not significantly better than model I (\(p\)-value = 0.0502). Between model II and model III, there is no significant difference (\(p\)-value = 0.7445). When analyzing model II by logistic regression, the analysis results are as presented in Table 32: Logistic regression analysis results with the variables camera type, rotation, and device used, allowing interactions of pairs.  
When looking at the results, it can be concluded that the influence of the device itself disappeared (\(p\)-value = 0.2649), but its interactions are still relevant (see \(p\)-values 0.0322 and 0.0286). This means that the device should be taken into account when analyzing the data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Type, Rotation and Device Used** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.53406 & 0.02376 & 22.479 & \(<\)\(2\times 10^{-16}\) \\ Perspective & 0.20095 & 0.03337 & 6.022 & \(1.72\times 10^{-9}\) \\ INC\_R & 0.15942 & 0.03339 & 4.774 & \(1.80\times 10^{-6}\) \\ Gear VR & \(-\)0.05639 & 0.05059 & \(-\)1.115 & 0.2649 \\ Perspective and INC\_R & \(-\)0.09672 & 0.04515 & \(-\)2.142 & 0.0322 \\ Perspective and Gear VR & 0.13418 & 0.06130 & 2.189 & 0.0286 \\ \hline \hline \end{tabular}
\end{table}
Table 32: Logistic regression analysis results with the variables camera type, rotation, and device used, allowing interactions of pairs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Type, Rotation and Device Used** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.53406 & 0.02376 & 22.479 & \(<\)\(2\times 10^{-16}\) \\ Perspective & 0.20095 & 0.03337 & 6.022 & \(1.72\times 10^{-9}\) \\ INC\_R & 0.15942 & 0.03339 & 4.774 & \(1.80\times 10^{-6}\) \\ Gear VR & \(-\)0.05639 & 0.05059 & \(-\)1.115 & 0.2649 \\ Perspective and INC\_R & \(-\)0.09672 & 0.04515 & \(-\)2.142 & 0.0322 \\ Perspective and Gear VR & 0.13418 & 0.06130 & 2.189 & 0.0286 \\ \hline \hline \end{tabular}
\end{table}
Table 31: Logistic regression analysis results investigating the variables camera type, rotation and device used.