Statistical Significance of Differences Magnesium Diboride Superconducting Transition Temperature Modeling

An extensive study has been done on a modeling approach that helps forecast the critical temperature of magnesium diboride (MgB2) materials. To determine the statistical importance of changes in modeling techniques, investigations must be able to consistently distinguish between systematic approach impacts and mistakes caused by modeling approach variance. In this study, researchers used analysis of variance and correlation analysis to determine the statistical significance of differences in modeling method variation. The resulting results were compared to the critical temperature estimated from lattice parameter change data using gaussian process regression (GPR), support vector machine (SVM), and linear regression with natural logarithm transformation (LRNLT).