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Optimum values of the independent parameters were determined by solving a multi-objective optimization problem obtained by using the proposed regression models for dependent parameters.
The generalized linear mixed model (GLMM) procedure was used to fit statistical models for dependent samples.
Cumulative logistic models were used for ordinal dependent items consisting of three categories and binary logistic models for dependent items having two categories.
Linear regressions will be constructed for continuous dependent variables, while logistic models will be used for dichotomous dependent variables and multilevel longitudinal models for dependent variables that are the object of repeated measures.
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Adjusted odds ratios (OR) and their 95% confidence intervals (95% CI) were estimated by means of a generalised linear mixed model for dependent binomial-type variables (case or control) [18].
Statistical models applicable for dependent data are then needed.
As mentioned above, four fixed-effects models for the dependent variables AZG, AZS, AMB, MED (i.e., outpatient services) and two random-effects models for the dependent variables HOS and SOM (i.e., inpatient services) are calculated.
Table 2: Summarizing results of the best models for the dependent variables Clinical Score, Colony Forming Units (CFU) and Somatic cell count (SCC).
Topics covered include fixed effects, random effects, differences-in-differences models, dynamic panel models, random coefficient models, models for qualitative dependent variables, and panel attrition.
Our regressions use two basic models for each dependent variable.
Primary analyses included factorial gender×SOB ANOVAs or binary logistic regression models for each dependent trait.
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