The results of inter- and intraobserver variability measurements are presented in Tables 3, 4, and 5. Table 3 presents the results of pairwise agreement using Kendall's tau-b analysis for interobserver variability as a coefficient of concordance along with confidence intervals (95% confidence level) constructed using bootstrap analysis of the samples (100 order changes).
In order to assess the accuracy of the design methods it is suggested to calculate the coefficient of confidence.
The response model evaluated in this study is significant, with a coefficient of determination R2 of 0.910 at a confidence level of 95%.
The 95% confidence interval and a coefficient of determination R of 0.93, (Radj. of 0.80) indicated that the model was statistically significant.
When applying the BIRADS classification with the modification described above, a kappa coefficient of 0.60 (0.55-0.65, 95% confidence interval) and a quadratic weighted kappa of 0.66 (0.62-0.70, 95% confidence interval) were found.
A validity coefficient of r=0.89 (95% confidence interval: 0.87-0.90) indicathatthat OES summary scores correlated highly with a global OE assessment.
The sample size of 40 participants was determined to provide sufficient power to find a correlation coefficient of 0.7, with confidence intervals ranging from 0.5 to 0.9.
With a beta coefficient of 0.891 [95% confidence interval (CI), 0.651−1.131), the percentage of the rural population appears to have the highest explanatory power, followed by GNI (−0.184; 95% CI, −0.237 to −0.131) and location within the Eastern Mediterranean region (−0.169; 95% CI, −0.280 to −0.058).
Consequently, our final model included only a single term for PbB (raised to the 0.5 power), with an adjusted coefficient of −27.4 [95% confidence interval (CI), −37.7 to −17.1].
The signal is found to correlate reasonably well with the Southern Oscillation Index (SOI) with a correlation coefficient of 0.68 at a confidence level of 95% for the stations.
