Keywords
Superconductor, critical temperature, regression, linear regression
Abstract
This project estimates a regression model to predict the superconducting critical temperature based on variables extracted from the superconductor’s chemical formula. The regression model along with the stepwise variable selection gives a reasonable and good predictive model with a lower prediction error (MSE). Variables extracted based on atomic radius, valence, atomic mass and thermal conductivity appeared to have the most contribution to the predictive model.
Semester
Spring 2024
Course Name
STA 5703 Data Mining 1
Instructor Name
Xie, Rui
College
College of Sciences
STARS Citation
Fiagbe, Roland, "Predicting Superconducting Critical Temperature Using Regression Analysis" (2024). Data Science and Data Mining. 19.
https://stars.library.ucf.edu/data-science-mining/19
Accessibility Status
PDF accessibility verified using Adobe Acrobat Pro Accessibility Checker
Included in
Applied Statistics Commons, Data Science Commons, Multivariate Analysis Commons, Probability Commons, Statistical Methodology Commons, Statistical Models Commons, Statistical Theory Commons