## 4 Experimental Results  
An image brightness regression model was used to analyze the screen image brightness relationship with the following variables:  
* Dependent variable (\(Y\)): Screen image brightness;
* Independent variables (\(X\)):
* Screen size;
* Screen recording brightness;
* Mode (2D or 3D);
* Photographic equipment \(EV\);
* Interactions between variables, as shown in Table 3.  
Depending on the variables' regression standardized residuals, it was determined whether the distribution of the sample was normal, for which the bell curve is called a completely normal distribution curve. Because of sampling errors, there was a gap between the actual observed-value histogram and the normal distribution curve (_i.e._, Figure 9). However, no extreme values beyond three standard deviations were found in this experiment. As a result, the sample values corresponded naturally with the normal distribution. The study then examined the variables' standardized regression residual error on the normal P-P diagram, which exhibits a 45\({}^{\circ}\) line from lower left to upper right (_i.e._, Figure 10). Therefore, the sample observations are approximately in line with the basic assumption, as shown in Tables 4-6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Type** & **Name** & **Values** \\ \hline Dependent & & Because of the nature of the luminance variables, there is no normal distribution, so a Box-Cox transform is used to convert the variable (\(\lambda=0.3\)), so that \(\varepsilon\) (Note 1) has a normal distribution. \\ \hline  & (1) Screen Size & 32, 47, and 52 inch \\ \cline{2-3}  & (2) Field Brightness & 3200 and 7500 lx \\ \cline{2-3}  & (3) 2D or 3D mode & 2D and 3D modes \\ \cline{2-3} Independent & & Converted using the camera’s shutter aperture combination. \\ Variables (\(X\)) & (4) Photographic & Converted using the camera’s shutter aperture combination. \\  & & \multicolumn{1}{c}{Equipment \(EV\)} & Conversion Formula: \(EV=\log_{2}\) (\(N^{2}\)/t), where \(N\) is the aperture (\(F\) value), and \(t\) is the shutter speed (s). \\ \cline{2-3}  & (5) Interactions between & \\  & Variables & The interactions between each variable. \\ \hline \hline \end{tabular} The risk-free rate, \(\varepsilon\), extracts a random sample from a normal distribution with a mean of 0 and a standard deviation of 1.  
\end{table} 
Table 3: Dependent and independent variables. Figure 9: Standardized residuals histogram. Table 4 shows the measures used in the brightness regression model. In this model, \(R^{2}\) is used to illustrate the explanatory power of the entire pattern. However, this measure tends to overestimate phenomena depending on the sample size; the smaller the sample, the more prone the model is towards overestimation. Therefore, the majority of researchers use \(\overline{R}^{2}\), which is the error variance and variable (\(Y\)) divided by the degree of freedom. Analysis of variance (ANOVA) is a particular form of statistical hypothesis testing that is widely applied in the analysis of experimental data (_i.e._, Table 5). Statistical hypothesis testing is a method of decision-making based on data. If the test results (calculated by the null hypothesis) fall within a certain likelihood of not being accidental, they are deemed to be statistically significant. For example, when the \"\(p\) value\", which is calculated from the data, is less than the defined critical significance level, the original hypothesis can be deemed invalid. A regression coefficient of 0 can indicate that the variable has no effect on the model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Brightness Regression Model** & \begin{tabular}{c} **Correlation** \\ **Coefficient (\(R\))** \\ \end{tabular} & \
\begin{tabular}{c} **Coefficient of** \\ **Determination (\(R^{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Adjusted Coefficient of** \\ **Determination (\(\overline{R}^{2}\))** \\ \end{tabular} \\ \cline{2-4}  & 0.995 & 0.990 & 0.990 \\ \hline \hline \end{tabular}
\end{table}