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Balance data were analyzed using a general linear model procedure with horse, period and treatment as main effects.

Serum Cu data were analyzed using the general linear model procedure with horse, treatment and period as main effects and time as the repeated variable.

Separate analyses were conducted at each scale using the generalized linear model procedure with a Poisson distribution in SPSS [ 39].

Mixed-effect regression analysis was implemented by using the mixed linear model procedure with restricted maximum likelihood (REML) estimation method.

Variability in PAT metrics and metabolic parameters across the five visits were investigated using the mixed model procedure, with participant treated as a random factor and visit treated as a fixed effect.

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Data were analyzed using mixed model procedures, with period, sequence, and treatment as fixed effects and herd as a random effect.

To model procedures with aftereffect, it is not sufficient to employ ordinary or partial differential equations.

Generalized Linear Model procedures with a log link function and Gamma distribution measured the association of health literacy with annual (2007 2009) patient-level VA medical, fee-basis and pharmacy costs, while other factors are controlled.

Analyses of variance were calculated with the generalized linear modeling procedure, with diagnostic group as the between factor (e.g., PTSD vs. no PTSD) and AMT-f cue valence (positive vs. negative) as the within-subject factor.

Emigration-dynamic variables, quorum thresholds, number of forward tandem runs, choosiness and cohesiveness indexes were compared among treatments and nests using SPSS general linear mixed model procedure (GLMM) with fixed factors 'Treatment', 'New nest site' (if applicable) and their interaction, and random factors 'Replicate' and 'Colony'.

The mixed model procedure deals with missing values by assuming them to be missing at random without removing the individual from the dataset.

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