Sentence examples for best multiple regression model from inspiring English sources
Model selection results suggested that the best multiple regression model for fecundity included 3 variables: female aggression, male aggression, and male territory size (Tables 2 & S1).
Nevertheless, the treatment group is involved in several of the best multiple regression analysis models (Table 4).
The best-fitting multiple regression model resulted in a determination coefficient (R) of 0.737, and a root mean square error (RMSE) of 0.076 (i.e. 1.09 μg/m).
The best-fitting multiple regression model resulted in a determination coefficient (R) of 0.704, and a RMSE of 0.132 (i.e. 1.14 μg/m).
When tested with an independent data set, the multiple regression model performed best with a higher coefficient of variance R2 (0.78 vs. 0.70), lower root mean square difference (RMSD) (12.92% vs. 13.05%) and the same mean bias difference (MBD) of −2.20%.
After extensive bottom-up and top-down multiple regression model building, the best model (first numeric column: total coefficient of determination R = 0.1083) included Y2H, but not ACMS, as a predictor of noise.
Significant factors were then entered into stepwise multiple regression model to determine the best predictors both at baseline and at discharge.
Three models were used: ridge regression best linear unbiased prediction (RR-BLUP), BayesC π (Habier et al. 2011), and a multiple regression model in which marker effects were estimated with ordinary least squares (OLS).
