Sentence examples for core regression model from inspiring English sources
Our core regression model, consisting of age at death, gender, and years of education, explained 3% and 7% of the variation in our pathological and cognitive traits, respectively.
We first added the four demographic covariates to the "core" regression model described above.
The core regression model did not include the exposure of interest.
After building up the core regression model for temperature and humidity-related hospital admissions, single pollutant was entered into the regression, and the effects of the pollutant on the day of admission and the previous 5 days (i.e. at lag 0, 1, 2, 3, 4 and 5) were examined to account for potential delays in disease incidence after important exposures.
The addition of WMn to the core regression models produced negligible changes in the associations between core model variables and intellectual function raw scores.
A core logistic regression model (model 1) was then built including all 14 variables that were identified from previous literature to be associated with maternal death.
Next, we constructed our "core" multivariable regression models where the outcome was phthalate metabolite concentrations and the independent variables were NHANES sampling cycle and urinary creatinine concentrations (to account for urinary dilution) (Barr et al. 2005).
In this paper, we introduce an efficient workflow to integrate the probabilistic neural networks (PNNs), as a nonlinear facies classification algorithm, into the generalized boosted regression model (GBM), as nonlinear modeling algorithm, for core permeability modeling and prediction.
To assess the association between the core items and global satisfaction, we used item 24 about overall satisfaction as the dependent variable in a regression model and the remaining nine core items as explanatory variables.
The individual core tensor as the output variable of the regression model is firstly extracted from the measured HRTFs.
Furthermore, we applied a stepwise logistic regression model to combine the three core genes to distinguishing DHAS from LDSDS.
