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The dependence was fitted with standard mathematical regression with a confidence interval better than 0.99.
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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 fitted results showed good agreement with the FE analysis results and experimental results, suggesting that the mathematical regression model may be directly used in a real-time system to predict characteristics of different brushes under varying operating conditions.
Mathematical regression models have been employed for theoretical prediction.
Response surface methodology based on a central composite rotatable design with three parameters, five levels, and 20 runs, was used to conduct the experiments and to develop the mathematical regression model by using of Design-Expert software.
The mathematical regression model relating powder forming process parameters to the shrinkage was developed.
This study develops a mathematical regression model to summarize the FE modeled results.
Doubling times were determined by mathematical regression using GraphPad Prism 4 software (GraphPad Software Inc, La Jolla, CA).
The traditional approach also involves regression with an implicit model function for n > 4 and its mathematical implementation is therefore not straight-forward.
The analysis was performed with Multiple matrix regression with randomization (MMRR), fitted with a Generalised Additive Model (GAM) regression.
For this, we used multiple regression with regularization parameters.
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