## 4 Results
### Results of Analyses of Effects of Two Factors
#### 4.2.5 Analysis of the Pair Camera Rotation and Device Used  
The next pair to be examined was the camera rotation and device used. For the camera rotation, the authors used the same groups that had been formed previously, NO_R and INC_R. 1062 tests were carried out for the group named \"NO_R, desktop display\", 151 for the group \"NO_R, Gear VR\", 1098 for the group \"INC_R, desktop display\", and 398 for the group \"INC_R, Gear VR\".  
Similarly to earlier comparisons, the numerical values of the average rates in the mentioned groups can be seen in Table 13, and the results of the logistic regression analysis are contained in Table 22.  
The reference point was \"NO_R, Desktop display\". As the sign of the estimation of the coefficient is negative, it can be seen that the probabilities of the correct answers are a bit smaller in the group \"NO_R, Gear VR\", but the difference is not significant (\(p\)-value = 0.385). The other two groups present significantly greater probabilities. There is a significant difference between \"NO_R, Gear VR\" and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Rotation and Contrast Ratio** & **Estimate** & **Standard Error** & \(z\) **Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.60941 & 0.01821 & 33.469 & \textless{}2\times 10^{-16}\) \\ INC\_R & 0.14922 & 0.03235 & 4.613 & \(3.97\times 10^{-6}\) \\ INC\_C & 0.09323 & 0.04483 & 2.080 & 0.0376 \\ INC\_R and INC\_C & \(-\)0.07651 & 0.05533 & \(-\)1.383 & 0.1667 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Logistic regression analysis results of the additive model using the variables camera rotation and contrast ratio, allowing interactions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resid.** & **Df Resid.** & **Dev** & **Df** & **Deviance** & **Pr (\textgreater{}Chi)** \\ \hline
1 & 2706 & 10,112 & - & - & - \\
2 & 2705 & 10,111 & 1 & 1.9179 & 0.1661 \\ \hline \hline \end{tabular}
\end{table}
Table 21: Comparison of the variables camera rotation and contrast ratio by ANOVA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Rotation and Contrast Ratio** & **Estimate** & **Standard Error** & \(z\) **Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.60941 & 0.01821 & 33.469 & \textless{}2\times 10^{-16}\) \\ NO\_R, INC\_C & 0.09323 & 0.04483 & 2.080 & 0.0376 \\ INC\_R, NO\_C & 0.14922 & 0.03235 & 4.613 & \(3.97\times 10^{-6}\) \\ INC\_R, INC\_C & 0.16594 & 0.02584 & 6.421 & \(1.36\times 10^{-10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 19: Logistic regression analysis results concerning the camera rotation and contrast ratio.  
\"INC_R, Gear VR\". If the authors apply the additive model with interactions, the same phenomenon can be seen, as presented in Table 23:  
As can be seen, the camera rotation has significant influence (\(p\)-value = \(2.97\times 10^{-6}\)), but the influence of the device used is not significant (\(p\)-value = \(0.3847\)) in itself. However, there is significant interaction at the 0.05 level of significance (\(p\)-value = \(0.0326\)). The reader can check the numerical values of the average rates in the involved groups in Table 14. Table 24 shows the results of their interactions.  
Due to \(p\)-value = \(0.0329\), the authors concluded that the model that takes the interactions into account provides a significantly better probability than the additive model.