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The data in the study by Schmidt and Moust (1995) was analyzed using structural equations modelling, a statistical method that allows causal hypotheses to be tested by comparing the structure of correlational data with a theoretical model.
Variables were combined, in the hope of attaining a synergistic increase in prognostic efficacy, using logistic regression analysis (LRA) modelling, a statistical technique that maximises binary classification accuracy using a linear combination of weighted input variables plus a constant term (Hosmer and Lemeshow, 1989).
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The investigated process was modeled using a statistical Weibull 2-component mixture model.
To determine the efficiency of PSO-ANFIS, GA-ANFIS and ANFIS models, a statistical analysis was performed.
In the final model, a statistical significance level of p < 0.05 was adopted.
A hybrid model consisting of a physical model and a statistical model based on historical plant operation data was proposed.
Hardy, D. A., Gussenhoven, M. S. & Holeman, E. A statistical model of auroral electron precipitation.
Hardy, D. A., Gussenhoven, M. S. & Brautigam, D. A statistical model of auroral ion precipitation.
Nesvizhskii, A. I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry.
We used logistic regression models as a statistical modeling strategy because of the dichotomous dependent variable.
The quasi-chemical model is a statistical mixing model in Gibbs free energy.
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