THE APPLICATION OF ANALYSIS OF VARIANCE TO DIFFERENT IRON‑BASED SUPERCONDUCTOR CRITICAL TEMPERATURE MODELING

Abstract

Recently, Iron-based superconductors have shown promising properties of high critical temperature and high upper critical fields, which are prerequisites for applications in highfield magnets. Extensive research has been conducted on a modeling approach that contributes to predicting doped Iron-based superconductor critical temperature from structural and topological parameters. Statistical significance of differences in modeling approaches requires studies that can reliably distinguish between systematic approach effects and errors resulting from modeling approach variation. In this work, we introduce analysis of variance (ANOVA) to assess the statistical significance of differences in modeling approach variation. Comparisons of obtained results with Iron-based modeling approach variation data of support vector machine (SVM) and linear regression with natural logarithm transformation (LRNLT) were presented.