Experimental results were statistically processed by the methods of variation statistics and multifactor analysis of variance (MANOVA) with the use of STATGRAPH statistic program kit.
Effects of sampling times, treatments and tissues on gene expression were analyzed using multifactor analysis of variance (ANOVA) followed by a Holm-Sidak post-hoc test when appropriate.
It deals with the factorial experiments that are carried out within blocks, an analog of the multifactor analysis of variance (ANOVA), and classifies repeated measures designs by the number of between-subject and within-subject factors.
Statistical design of experimental methodology based on Taguchi orthogonal design has been used to study and optimize various parameters using multifactor analysis of variance (ANOVA).
Methods include linear regression, inference, model assumption evaluation, the likelihood approach, matrix formulation, generalized linear models, single-factor and multifactor analysis of variance (ANOVA), and a brief foray into nonlinear modeling.
Statistical comparisons were made using a multifactor analysis of variance (ANOVA SPSSS version 18, IBM).
We used multifactor analysis of variance to assess the significance of factor effects such as animals, slides, and doses.
The multifactor analysis of this study identified age, liver and cardiac involvement as independent prognostic risk factors.
Cumulative fractional positive results of all low- and medium-concentration samples (<86,197 copies/mL) were subjected to multifactor analysis of variance, which eliminated influence of other defined factors in each analysis.
To compare PCS and MCS between emergency department patients and data in the German Federal Health Survey, a multifactor analysis of variance (ANOVA) was conducted with "setting", "gender" and "age-group" as fixed factors.
