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Mathematical regression models have been employed for theoretical prediction.
The mathematical regression models of the wear volume and average COF were derived from the analysis of variance (ANOVA).
Mathematical regression models are developed to predict the ultimate tensile strength (UTS) and tensile elongation (TE) of the dissimilar friction stir welded joints of aluminium alloys 2024-T6 and 5083-H321, and they are validated.
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This study develops a mathematical regression model to summarize the FE modeled results.
The mathematical regression model relating powder forming process parameters to the shrinkage was developed.
Correlation analysis of the mathematical regression model indicated that a quadratic polynomial model could be employed to characterize extraction process for the total flavonoids.
Correlation analysis of the mathematical regression model indicated that quadratic polynomial model could be employed to optimize the extraction of oil from orange peel.
The results showed that the experimental data could be fitted to a quadratic polynomial model using correlation analysis of the mathematical regression model.
Additionally, the novel textural features were used to design a mathematical regression model for providing a quantitative estimation of OA severity.
Measured OA severity values, as expressed by HJS-narrowing, correlated highly (r = 0.85, p < 0.001) with the predicted values by the mathematical regression model.
The optimum mathematical regression model for the theoretical deflection basin without interference was analysed using the probabilistic density method, which was then applied to remove the noise information of the in situ measured deflection basin.
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