## 4 Results
### Results of Analyses Investigating the Effects of Three Factors
#### 4.3.4 Analysis of the Triplet Camera Rotation, Contrast Ratio, and Device Used  
The last triplet to be analyzed is the camera rotation, contrast ratio and the device used. The former two are grouped as before. First, the authors formed \(2\times 2\times 2=8\) groups according to the levels of the variables. The statistical characteristics of the resulting groups are presented in Table 18. The results of the logistic regression analysis for these 8 groups are presented in Table 35. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Type, Contrast Ratio** & & & & \\
**and Device Used** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.52553 & 0.02194 & 23.951 & \(<\)\(2\times 10^{-16}\) \\ Perspective & 0.23264 & 0.03021 & 7.700 & \(1.36\times 10^{-14}\) \\ INC\_C & 0.19581 & 0.03216 & 6.088 & \(1.14\times 10^{-9}\) \\ Gear VR & 0.05810 & 0.02816 & 2.063 & 0.039088 \\ Perspective and INC\_C & \(-\)0.17397 & 0.04507 & \(-\)3.860 & 0.000113 \\ \hline \hline \end{tabular}
\end{table} 
Table 34: Logistic regression results of the additive model with the variables camera type, contrast ratio, and the device used, allowing interactions of pairs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Type, Contrast Ratio and Device Used** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.52553 & 0.02194 & 23.951 & \(<\)\(2\times 10^{-16}\) \\ Perspective & 0.23264 & 0.03021 & 7.700 & \(1.36\times 10^{-14}\) \\ INC\_C & 0.19581 & 0.03216 & 6.088 & \(1.14\times 10^{-9}\) \\ Gear VR & 0.05810 & 0.02816 & 2.063 & 0.039088 \\ Perspective and INC\_C & \(-\)0.17397 & 0.04507 & \(-\)3.860 & 0.000113 \\ \hline \hline \end{tabular}
\end{table} 
Table 33: Logistic regression analysis results of the effects of the camera type, contrast ratio, and device used. The results show that there is no detectable difference between \"NO_R, NO_C, desktop display\" and \"NO_R, NO_C, Gear VR\" (\(p\)-value = 0.82046). The cut is not significant in the row of \"NO_R, INC_C, Gear VR\". This requires clarification, as the average rates of correct answers are 0.602 and 0.561. However, with \"NO_R, INC_C, Gear VR\", the available data is very low (23), and the dispersion is high. Further investigations (tests) are needed to be able to reject the hypothesis of the equality of the average rates. In other cases, there is a significant improvement. The largest improvement is in the case of \"INC_R, INC_C, Gear VR\". Moreover, the results of this case are also significantly better than the results of any other case. When comparing the model investigating the effects of the variables without interactions (I), the model investigating the effects of variables allowing the interaction of rotation and used devices (II), and the model allowing the interactions of all three variables (III), the following conclusions can be drawn: II is significantly better than I (\(p\)-value = 0.02568), III is significantly better than I (\(p\)-value = 0.0001853) and is also significantly better than II (\(p\)-value = 0.0006448). When using model III, the logistic regression analysis yields results as shown in Table 36:  
According to this table, the unique influence of the device disappears, but it has an interaction with the contrast ratio, and even triple interactions can be detected. Concluding the analyses of the effects of triplets of variables, the following factors should be considered: camera type, rotation, contrast ratio, and the device used. The results show that the most important interactions are those between:  
* Camera type-Camera rotation
* Camera type-Contrast ratio
* Camera rotation-Device used
* Camera rotation-Contrast ratio-Device used