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Consequent parameters are obtained using linearized plant models and linear design techniques.
Ordered logit models and linear regressive model were set up to analyze impact factors of the three indicators, respectively.
Thus, one must differentiate between linear statistical models and linear models that also imply straight-line relationships.
That is done via several types of models namely input-output models, computable general equilibrium models, and linear and non-linear programming (optimization) models [20].
PF is an approximation technique for estimation problems where the variables involved do not hold Gaussianity uncertainty models and linear dynamics.
Predictor class selection categories of memoryless sources, Markov models, and linear regression models along with machine-based techniques were detailed based on current SOP literature proposals.
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Linear models and generalized linear models (logistic regressions and log-linear models) of the genetic effects of the risk factors are often used to define statistical interactions.
Statistical analyses Associations were examined using cross tabulations, log-linear modeling, and linear regressions.
The experimental results are compared with finite element analysis (FEA) model and linear analytical model.
Two different modeling approaches for soil foundation interaction, nonlinear Winkler model and linear lumped-parameter model, were investigated.
Tzagkarakis used a sinusoidal model and linear prediction to parameterize the separate spot microphone channels, then downmixed the residual signals.
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