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This review will outline the involvement of the different macrophage subsets throughout atherosclerosis progression and in models of regression.
For each outcome variable, three models of regression were performed: 1) Crude Model with no confounding variables, 2) Complete Model including all potential confounders and 3) Simple Model including only confounders that met the change-of-estimate criterion set at 20%[30]0].
The quantitative methods used included random effects models and fixed effects models of regression coefficients, odds ratios or hazard ratios.
16, 17 For each intervention group, three models of regression were used to explain adherence or failure to adhere.
ROC analysis was used to represent the probability score predicting early sepsis diagnosis from equations and to compare different models of regression.
Since the variables had different numbers of missing values and different variables entered into the final models of regression analyses, the sample sizes differ for each analysis.
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The results of Levene's test showed that the assumption of homoscedasticity was not valid for the dependent variables, so appropriate generalised models of regressions were used.
Focusing on prognostic markers in oncology, they discuss issues of statistical model building in the framework of regression models and of classification and regression trees.
We consider the model of regression with random design, where the design density is unknown.
According to Orr [22], Marcek [18], the most used model of regression neural network type is the RBF neural network.
It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy.
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