To evaluate the regression equation model, allele and grain size-related trait data were gathered from an additional 34 germplasms of diverse origin and grain size (Additional file 5).
The influences of the main mechanical parameters were evaluated, and the regression model can be used to predict the relationships between the pressure drop (Δp) and the three main mechanical parameters of the isolation tool in the plug process.
When change in fitness was evaluated in the regression model with change in A1C (model K), we found that each predicted hs-CRP change.
Interactions among age, diabetes duration, and HbA1c were evaluated in the regression model, and no interaction terms were found to be statistically significant (all P values >0.01).
To evaluate the Bayesian regression model, the amount of ET estimated by this model was plotted versus the calculated values.
To evaluate the robust regression model, the amount of ET estimated by this model was plotted versus the calculated values.
Interaction effects were examined and evaluated in the regression models by including a product term.
However, relative survival was also evaluated in the regression models.
When data from HbSS group alone were evaluated in the regression models, only hs-CRP stayed in the models contributing 13% to the overall association.
The decline in O3 was attributed to the marked decline in city traffic during the games, but associations with other mobile source emissions were not evaluated in the regression models.
