… the mathematical model (hyperbolic or derived linear models) … for the determination of CP
and W′, whereby power output … of work performed above this end-test power equates to W′. …

… Regression analysis estimates dependent 'y' variable value due to the range of independent
… linear regression and polynomial regression in this paper that better fits the predictive model…

… , and other models based on calculation formula and other factors. Specifically, … BA models:
linear regression models, power function models, non-linear models and polynomial models. …

… To see the specific operational details that were discussed above, here we present renewable
estimation in the Gaussian linear model. For the linear model, the renewable estimation …

… setting (age prediction), the 3D convolutional architecture here achieved R 2 = 0.61 ±
0.56/0.66 (coefficient of determination) and our linear model achieved R 2 = 0.61 ± 0.54/0.67 (L2-…

… estimate for each time point in the epoched EEG data. Importantly, therefore, this approach
fits a separate linear model … to model non-linear effects within the linear regression framework…

… estimation equation/model through regression, which is not suitable for accurate SOC … linear
regression algorithms. Taking the one-forward-step prediction accuracy of linear regression …

… the ordinary linear regression by allowing the output function φ ( z , A ) to be nonlinear and/or
… We focus on determination of phase transitions in performance as we vary parameters of …

… interpolation methods were employed to estimate the EC 50 values. Linear regression and
direct interpolation results showed deviations for non-linear models in the prediction of EC 50 …

… estimates that are actually more interpretable and practical than linear regression to improve
accuracy in modeling human … for nonlinear ML models than linear models using both spatial …