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First, we chose the age-related miRNAs by polynomial regression models.
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Very high regression coefficient between the variables and the response (R2 = 0.9930) indicated excellent evaluation of experimental data by polynomial regression model.
Very high regression coefficient between the variables and the responses: decolorization and COD removal were R2 = 32.69% and R2 = 52.25%, respectively indicating an excellent evaluation of experimental data by polynomial regression model.
Their corresponding experimental data could be evaluated excellently by second order polynomial regression models and the two models were also examined based on the analysis of variance and t test statistics, respectively.
Zou et al. (2007) and Kazemzadeh et al. (2009a, b) considered cases when the profiles can be characterized by multiple and polynomial regression models respectively.
In this article, we consider robust designs for approximate polynomial regression models, by applying the theory of canonical moments.
Normalization: signal drift and batch-effects, which are two major source of bias in MS data (van der Kloet et al., 2009 ), can be corrected by fitting linear or local polynomial regression models to quality control samples.
Comparison of measured FPIs with those predicted by the multi-linear, logarithmic and polynomial regression models showed good agreement with correlation coefficients of 0.87, 0.87 and 0.86, respectively.
The coefficients of the second-order polynomial regression models were fitted by solving linear least squares problems.
In this article we consider D-optimal designs for polynomial regression models with low-degree terms being missed, by applying the theory of canonical moments.
By using canonical moments, in 1980, Studden found Ds-optimal designs for polynomial regression models.
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