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To identify factors with an influence on the visual prognosis, multiple linear regression analysis was performed, with the effects of profile, linearity, interaction, and collinearity on the multiple linear models being examined by regression diagnostic analysis.
Empirical models were formulated based on multiple linear models.
The piecewise linear regression allows multiple linear models to be fitted, and where the lines meet can be used to identify breakpoints where the slope of the linear function changes.
Mini-max optimisation algorithms are used for the design of robust controllers based on the multiple linear models.
The control algorithm is a receding horizon scheme with a quasi-infinite horizon objective function which has finite and infinite horizon cost components and uses multiple linear models in its predictions.
To model nonlinear hybrid systems, Nandola and Bhartiya [3] proposed a multiple linear model approach wherein multiple linear models are used to describe the dynamic behavior in each mode of the hybrid system.
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A multiple linear model was used to test for difference between genotype groups assuming an additive model in quantitative trait studies.
As such, a statistical analysis employing a multiple linear modelling procedure is considered beneficial.
The General Linear Model (GML) and Multiple Linear Model (MLM) implemented in TASSEL software [ 63, 80] were applied.
Subgroup analysis using the multiple linear model was performed using type of dietary iron (heme versus non-heme).
Further, a multiple linear model was used to estimate the explanatory power of independent variables (demographic, disease related, VO2peak) on restrictive pulmonary impairment (FVC%) [ 40, 41].
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