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Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level.
Two statistical models have been tested: multi linear regression (MLR) and neuronal networks, and their efficiencies were compared in terms of prediction skill.
From a multi linear regression analysis, we develop a job satisfaction model built on factors of human resource policies, safety, ergonomics, air quality, thermal comfort and disturbing equipment.
Response surface methodology (RS M has been applied for developing the models using the techniques of design of experiments and multi linear regression analysis.
The results of the developed GP models are compared with those of a commonly used multi linear regression (MLR) model and the advantages of the proposed GP model over the conventional method are highlighted.
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The authors' prior attempts to re-engineer published QSAR or QSPR models suggest that most results beyond the simplest (multi-) linear regression models are not recoverable, least usable for practical applications.
The optimal chromatographic conditions were determined by multi-linear regression.
The best seven- and eight-parameter multi-linear regression models showed good predictive ability.
Five QSAR models were generated using Multi-Linear Regression and Genetic Function Approximation (GFA).
Compared to a multi-linear regression model, the ANN revealed not to perform significantly better.
Therefore, an artificial neural network (ANN) and multi-linear regression models are used.
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