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By utilizing the multiple linear copositive Lyapunov function (MLCLF) approach, the ADT switching is introduced to tackle the state estimation of the underlying system.
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Thus, the multiple linear regression model (also Vanilla Model in [11]) is utilized as an outliers detector in the second stage.
However, the multiple linear regression model is more usable.
The multiple linear regression (MLR) is capable of deriving the linear QSAR based on them.
Table 4 presents the results of the multiple linear regression.
Results of the multiple linear regressions are in Table 3.
Table 2 summarizes results from the multiple linear regression models.
R2 of the multiple linear regression model = 0.24.
Adjusted R2 of the multiple linear regression model = 0.25.
The multiple linear regression is based on the final linear model, respectively.
The multiple linear regression method is more actual and effective than linear fitting method.
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