In the present study, it has been shown that organic acids in the TCA cycle can be modeled using various techniques, viz., factor analysis, multiple linear regression, system analysis, and neural networks.
The third experiment compared the performance of the proposed EPCNN model with other models like BPN, TSK fuzzy system, multiple regression analysis and showed that EPCNN can provide a very accurate prediction of the stock price index for most of the data.
Similar variations by province, sector, nursing category, and unit were found in the multiple regressions of health system consequences relevant to the moonlighting group only (Table 4).
This study aims to investigate the human behavior, and environmental factors influencing to the distribution, and to build a model using stepwise multiple regression analysis with geographic information systems on environment and climate data.
As an alternative, indirect models i.e., Mamdani fuzzy inference system (MFIS) and multiple regression analysis (MRA) are uses to determine E in this research.
While meaningful relationships were not obtained from the simple regression analyses, both multiple regression analyses and the fuzzy inference system exhibited good predictive performance.
The research question is addressed using a geographic information system (GIS) and multiple regression analyses of count data.
However, in literature, the non-linear multiple regression and the adaptive neuro-fuzzy inference system have not been used for the weathering classification yet.
The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system.
Based on multiple regression analyses, the strongest relationship was found between mating system and perenniality: annual or biannual species present lower outcrossing rates than perennial species (table 1).
