## 4 Results and Discussion
For each of the six scales, the mean value was calculated (Table 1). Values between \(-0.8\) and \(0.8\) represent a more or less neutral evaluation, values greater than \(0.8\) represent a positive evaluation and values less than \(-0.8\) represent a negative evaluation. The range of scales is between \(-3\) (terribly bad) and \(+3\) (extremely good). The values in Table 1 were automatically represented in a graph, in which the ranges for a positive, neutral, or negative value were highlighted in different colors (Figure 7). The error bars represent the 5% confidence intervals for the scale means. In other words, they represent the probability that the true value of the scale mean lies outside this interval is less than 5%. The dimension of the error bars depends on the number of respondents and on the level of agreement between the respondents. Thus, the more the participants that filled out the questionnaire agree concerning their evaluation of the product the smaller the dimension of the error bars typically is [36]. As this is the first time this questionnaire was used to evaluate our MR application, in order to have a more objective opinion, the results were compared with a benchmark [37], which allows the results to be compared with a large data set. The data set used contains data from 21,175 people from 468 studies concerning different products. Ranges of values were identified to classify each of the six scales into one of the following categories: (1) Excellent, (2) Good, (3) Above average, (4) Below average, and (5) Bad. The ranges identifying the various categories are summarized in Table 2.  
By comparing the mean values obtained in this study with the reference ranges, a category was assigned to each UEQ scale (Figure 8). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **UEQ Scales** & \\ \hline
**Scale** & **Mean** & **Confidence Interval** \\ \hline Attractiveness & 2.133 & 0.263 \\ Perspicuity & 1.533 & 0.373 \\ Efficiency & 1.733 & 0.251 \\ Dependability & 1.817 & 0.315 \\ Stimulation & 2.167 & 0.354 \\ Novelty & 2.433 & 0.237 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean values and confidence interval for each scale (Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty). The average value for each UEQ scale has been superimposed to the graph. As shown in Figure 8, \"Excellent\" was assigned to 4 scales (Attractiveness, Dependability, Stimulation and Novelty), indicating that the application developed is an innovative, safe and user-friendly product. Instead, Perspicuity presents a slightly lower value, meaning that the subjects encountered difficulties to learn to use the application. This result can be explained considering that the subjects had a minimum experience with the HoloLens and, therefore, part of the difficulties was related to the use of this device and not to the application itself. Despite this Perspicuity value, the application is still \"Above Average\" in terms of ease of learning. The category \"Good\" was assigned to Efficiency so users judged the application \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Ranges of the 5 Categories for Each UEQ Scale**} \\ \hline
**Scale** & **Excellent** & **Good** & **Above Average** & **Below Average** & **Bad** \\ \hline Attractiveness & \(\geq\)1.75 & \(\geq\)1.52 \(<\)1.75 & \(\geq\)1.17 \(<\)1.52 & \(\geq\)0.70 \(<\)1.17 & \(<\)0.70 \\ Perspicuity & \(\geq\)1.90 & \(\geq\)1.56 \(<\)1.90 & \(\geq\) 1.08 \(<\)1.56 & \(\geq\)0.64 \(<\)1.08 & \(<\)0.64 \\ Efficiency & \(\geq\)1.78 & \(\geq\)1.47 \(<\)1.78 & \(\geq\)0.98 \(<\)1.47 & \(\geq\)0.54 \(<\)0.98 & \(<\)0.54 \\ Dependability & \(\geq\)1.65 & \(\geq\)1.48 \(<\)1.65 & \(\geq\)1.14 \(<\)1.48 & \(\geq\)0.78 \(<\)1.14 & \(<\)0.78 \\ Stimulation & \(\geq\)1.55 & \(\geq\)1.31 \(<\)1.55 & \(\geq\)0.99 \(<\)1.31 & \(\geq\)0.50 \(<\)0.99 & \(<\)0.50 \\ Novelty & \(\geq\)1.40 & \(\geq\)1.05 \(<\)1.40 & \(\geq\)0.71 \(<\)1.05 & \(\geq\)0.30 \(<\)0.71 & \(<\)0.30 \\ \hline \hline \end{tabular}
\end{table}