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Therefore, revised model is developed using regression analysis.
Therefore, quadratic models were developed using regression analysis to predict Rw and ηCOD.
Two capacity prediction models are developed using regression and neural networks (NNT).
Also the experimental results are validated with the analytical model developed using regression analysis computed using Minitab software.
Correlations are developed using regression analysis to understand the effect of these parameters on effective thermal conductivity.
A parametric embodied carbon prediction model was developed using regression analysis to estimate embodied carbon when only minimal design information is available and with less uncertainty.
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A multivariate logistic regression model was developed using stepwise regression (forward selection).
Seven prediction models were developed using logistic regression, linear discriminant analysis, classification and regression trees, k nearest neighbours, artificial neural networks, support vector machines and random forests.
Finally, mathematical models for both, positive springback and negative springback, were developed using the regression analysis.
Based on this information, two converter charts were developed using multi regression analysis method.
Second, the CSI models were developed using logistic regression technique by incorporating the selected risk factors.
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