## 5 Results
Figures 5 and 6 present unreliability functions for BD data as exponential-1P and Weibull functions under 130 \({}^{\circ}\)C and 960 h of degradation. The coefficient of determination for Figure 5 is 0.45. Figure 6 shows graphs fitted using [PERSON]'s 2P and 3P. The dotted line in Figure 6 represents a guide line for visually estimating the parameters of the Weibull distribution function. The point where the horizontal dotted line meets the Weibull function represents the scale parameter. Dotted lines with a slope are for estimating the value of the shape parameter through the slope of the Weibull function. The 2P-Weibull has a coefficient of determination of 0.929, and the 3P-Weibull has a higher coefficient of determination at 0.935. In both Tables 3 and 4, the exponential functions consistently exhibit lower values than the other distributions, regardless of the number of parameters used. This may be unsuitable for evaluating degraded specimens because the corresponding function does not account for the change in the failure rate over time. Tables 3 and 4 demonstrate that as the number of parameters increases, both the R\({}^{2}\) and MD values improve for the exponential and Weibull functions, suggesting an enhanced goodness of fit. However, the selection of an appropriate number of parameters for the distribution model should consider AIC and BIC values. Figure 5: Reliability plot after thermal degradation at 130 \({}^{\circ}\)C after 960 h of exp-1P distribution. Figure 6: Unreliability plot after thermal degradation at 130 \({}^{\circ}\)C after 960 h of Weibull distribution. Figure 7 depicts the coefficients of determination of five types of statistical functions according to the degradation time. Log-normal and Weibull-2P and -3P have a coefficient of determination of more than 0.8 under all conditions, rendering them suitable for interpreting BD data. Among the statistical functions analyzed, exponential functions have the lowest coefficient of determination and are not suitable for interpreting BD data. Figure 8 depicts graphs illustrating mean differences of five types of statistical functions according to the degradation time. A large mean difference implies that the data are not suitable for the distribution function, and the coefficient of determination is also low. Among the statistical functions analyzed, the exponential function is not suitable for interpreting BD data. According to the results of Figures 7 and 8, both the coefficient of determination and the MD tend to increase the fitness of the Weibull function as the degradation time increases. In addition, there is a section that reverses when comparing the log-normal function and the Weibull function. After the reversing section, the fitness of the Weibull function is the highest. Table 5 lists the AIC and BIC values for selecting the number of parameters in the Weibull distribution. Additionally, the suitability of Weibull-3P was analyzed using these values. If the AIC and BIC values for the Weibull-3P model were lower than those for the  
Figure 8: Changes in the mean differences of exponential function, Weibull distribution function, and logarithmic normal function according to degradation time at 130 \({}^{\circ}\)C.  
Figure 7: Changes in the coefficient of determination of exponential function, Weibull distribution function, and logarithmic normal function according to degradation time at 130 \({}^{\circ}\)C.  
Weibull-2P model, the Weibull-3P model was considered appropriate. According to the analysis results, only three of the \(11\) conditions were found to be suitable for Weibull-3P. Considering the coefficient of determination, the most suitable statistical distribution for analysis under each degradation condition was selected, and the parameter values at that time are shown in Table 6. The selected distributions have a high coefficient of determination. Therefore, when interpreting BD data with a statistical model, it can be interpreted by minimizing the error between the observed and estimated values.