## 3 Results
### Residual Analysis  
Raw data must be checked against residuals over the regression model, and some requirements on residuals should be met, namely,  
* var(\(\varepsilon_{i}\)) = \(\sigma^{2}\): homoscedasticity, i.e., homogeneous variance of residuals along the linear model; \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & \multicolumn{6}{c}{**Variance Proportions**} \\ \hline
**Eigenv.** & **Cond. I** & **(Constant)** & **Usefuln.** & **Clarity** & **Beauty** & **Intuit.** & **Inform.** \\ \hline
5.808 & 1.000 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\
0.064 & 9.558 & 0.63 & 0.02 & 0.02 & 0.12 & 0.08 & 0.00 \\
0.051 & 10.631 & 0.00 & 0.03 & 0.09 & 0.70 & 0.09 & 0.01 \\
0.037 & 12.561 & 0.36 & 0.56 & 0.00 & 0.13 & 0.07 & 0.08 \\
0.025 & 15.295 & 0.00 & 0.33 & 0.05 & 0.04 & 0.37 & 0.47 \\
0.015 & 19.372 & 0.00 & 0.05 & 0.84 & 0.01 & 0.40 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Coefficient matrix of variance decomposed in its principal components with eigenvalues, condition indices, and variance proportion at the component level. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Sum Sq.** & **df** & **Mean Sq.** & **F** & \(p\)**-Value** \\ \hline Corrected Model & 866.79 & 341 & 2.54 & 12.111 & 0.000 \\ Clear*Beautiful & 3.766 & 14 & 0.269 & 1.282 & 0.217 \\ Informative*Beautiful & 2.057 & 13 & 0.158 & 0.754 & 0.709 \\ Intuitive*Beautiful & 3.052 & 14 & 0.218 & 1.039 & 0.414 \\ Useful*Beautiful & 7.786 & 14 & 0.556 & 2.650 & 0.001 \\ Clear*Informative & 3.340 & 8 & 0.417 & 1.989 & 0.048 \\ Intuitive*Clear & 1.462 & 9 & 0.162 & 0.774 & 0.641 \\ Useful*Clear & 4.394 & 11 & 0.399 & 1.903 & 0.039 \\ Intuitive*Informative & 1.678 & 6 & 0.280 & 1.332 & 0.243 \\ Useful*Informative & 2.045 & 10 & 0.204 & 0.974 & 0.466 \\ Useful*Intuitive & 2.455 & 12 & 0.205 & 0.975 & 0.473 \\ Clear*Informative*Beautiful & 0.742 & 4 & 0.186 & 0.884 & 0.474 \\ Intuitive*Clear*Beautiful & 0.216 & 4 & 0.054 & 0.257 & 0.905 \\ Useful*Clear*Beautiful & 0.934 & 3 & 0.311 & 1.483 & 0.219 \\ Intuitive*Informative*Beautiful & 0.353 & 2 & 0.177 & 0.841 & 0.432 \\ Useful*Informative & 0.515 & 6 & 0.086 & 0.409 & 0.873 \\ Useful*Influitive*Beautiful & 0.638 & 6 & 0.106 & 0.507 & 0.803 \\ Intuitive*Clear*Informative & 0.361 & 2 & 0.180 & 0.860 & 0.424 \\ Useful*Clear*Informative & 0.969 & 5 & 0.194 & 0.923 & 0.466 \\ Useful*Intuitive*Clear & 0.118 & 2 & 0.059 & 0.282 & 0.755 \\ Useful*Intuitive*Informative & 0.251 & 2 & 0.126 & 0.598 & 0.550 \\ Intuit*Clear*Inform*Beaut & 0.000 & 0 & & & \\ Useful*Clear*Inform*Beaut & 0.000 & 0 & & & \\ Useful*Influit*Clear*Beaut & 0.000 & 0 & & & \\ Useful*Influit*Clear*Beaut & 0.069 & 1 & 0.069 & 0.330 & 0.566 \\ Useful*intuit*Clear*Inform & 0.000 & 0 & & & \\ Useful*Influit*Clear*Inform*Beaut & 0.000 & 0 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tests of between-subject effects. Dimension names abbreviated by using corresponding adjectives. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Toler.** & **VIF** \\ \hline Usefulness & 0.491 & 2.035 \\ Clarity & 0.265 & 3.774 \\ Beauty & 0.519 & 1.926 \\ Intuitiveness & 0.318 & 3.148 \\ Informativity & 0.345 & 2.895 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multicollinearity analysis. Note: VIF, variable inflation factor. * \(\text{Cov}(\varepsilon_{i},\,\varepsilon_{j})=0,\,\text{i}\
eq\text{j}\): independence, i.e., absence of autocorrelation of residuals or of meaningful patterns in the residuals;
* \(\varepsilon_{i}\sim\text{N}(\mu,\sigma)\): normality, e.g., residuals should be normally distributed. Figure 7 shows the linear relationships of each explanatory variable with the outcome variable, and the relation between residuals and predicted values of the multiple-regression model. A best-fit line was drawn in each scatterplot. The scatterplot of standardized predicted values showed that the data met the assumptions of variance homogeneity. The data met the assumption of independent errors (Durbin-Watson value = 1.93).