期刊名称：Indian Journal of Computer Science and Engineering
印刷版ISSN：2231-3850
电子版ISSN：0976-5166
出版年度：2019
卷号：10
期号：6
页码：177-183
出版社：Engg Journals Publications
摘要：The regression analysis plays a vital role in forecasting, estimating and predicting the material science domain. Inthis research work measures a statistical model to estimate the critical temperature of superconductor. This criticaltemperature formulated by using the superconductor’s chemical formula. The statistical model has given severalmeasurements like Correlation Co efficient, Mean Absolute Error (MAE), Root Mean Squared Error (RMSR),Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). These measurements extracted basedon atomic mass (AM), atomic radius (AR), valence(V), thermal conductivity (TC), and electron affinity (EA)contribute the most to the model’s predictive accuracy. This research work focuses the comparisons of variousmeasurements namely Correlation Co efficient, Mean absolute error, Root Mean squared error, Relative absoluteerror, Root relative squared error and also time taken to build the model of leading regression algorithms likeLinear and Simple Linear regression models for superconductor.
关键词：Conductivity; Simple Linear Regression; Atomic Radius; Linear Regression; valence; Electron;affinity; Correlation Coefficient and Atomic Mass.
