## 4 Results
### Results of the Analyses of a Single Factor's Effects
#### 4.1.2 Analysis of the Camera Field of View  
The second factor to be analyzed was the virtual camera's field of view. The default field of view in Unity is 60\({}^{\circ}\), but this value can easily be changed in the application. The authors were interested in multiple fields of view, such as 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), 90\({}^{\circ}\). 1049 tests were done with 45\({}^{\circ}\), 120 with 60\({}^{\circ}\), 134 with 75\({}^{\circ}\), and 115 with 90\({}^{\circ}\). 1291 tests were done with an orthographic camera. The numerical rates belonging to the groups mentioned are shown in Table 2. The results of the logistic regression analysis can be seen in Table 2  
The basis value was \(-1\), belonging to orthographic camera type. All coefficients are estimated to be positive. The \(p\)-values (in the Pr(\(>\)\(\lvert z\rvert\)) column) suggest that every probability is significantly greater in the case of the perspective camera's field of view options (45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), 90\({}^{\circ}\)). Moreover, due to the results presented in Table 1, the authors are aware that the perspective camera produces better probabilities. Therefore, the authors eliminated the data belonging to the value of \(-1\) and investigated whether the different levels of the fields of view in case of perspective camera type result in different probabilities. This means that the authors were interested in the results when the \(-1\) is taken out. The results of the analysis based on this restricted data set are contained in Table 3.  
The basic level was actually 45\({}^{\circ}\). The signs of the estimated coefficients show that each further level is better, but, except for 90\({}^{\circ}\), the difference is not significant. In the case of 90\({}^{\circ}\), on the basis of \(p\)-value = 0.0225, the 90\({}^{\circ}\) field of view presents a significantly better probability of correct answers than the others, at the level of 0.05.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Type** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\(>\)\(\lvert z\rvert\))** \\ \hline Intercept & 0.62452 & 0.01597 & 39.113 & \(<\)\(2\times 10^{-16}\) \\ Perspective & 0.15670 & 0.02239 & 6.999 & \(2.57\times 10^{-12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of logistic regression by investigating the effect of camera type.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Field of View** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\(>\)\(\lvert z\rvert\))** \\ \hline Intercept & 0.62452 & 0.01597 & 39.113 & \(<\)\(2\times 10^{-16}\) \\
45\({}^{\circ}\) & 0.14423 & 0.02423 & 5.952 & \(2.64\times 10^{-9}\) \\
60\({}^{\circ}\) & 0.14988 & 0.05595 & 2.679 & 0.00739 \\
75\({}^{\circ}\) & 0.15676 & 0.05351 & 2.930 & 0.00339 \\
90\({}^{\circ}\) & 0.27871 & 0.05830 & 4.781 & \(1.75\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the logistic regression analysis concerning the camera field of view as variable.  
However, the difference is not significant at the level 0.01. As the amount of data is quite large, the authors accept that the effect of the variable named Field of View is not significant in the case of the perspective camera type.