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One-way ANOVA was used to determine statical significance for multiple groups analysis.
To determine differences between multiple groups, analysis of variance (ANOVA) was used with post hoc comparisons using Fisher's method.
For comparison of multiple groups, analysis of variance on ranks was used and a Bonferroni correction was applied.
For comparison of multiple groups, analysis of variance (ANOVA) followed by Fisher's protected least significant difference post hoc test was used for parametric data, when a significant difference was found.
For comparisons of multiple groups analysis of variance (ANOVA) with Dunnett post-test for parametric data or the Friedman test with Dunns post-test for non-parametric data was performed.
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A nested model strategy allowed us to compare competing models and a multiple group analysis was conducted to test for moderation.
Employing latent growth curve modeling and using multiple group analysis, the study provides empirical evidence for the causality between service infusion strategies and manufacturers' profit trajectories.
All data are expressed as mean±standard error (SE) and compared by one-way analysis of variance (ANOVA) and Student-Newman-Keuls test for multiple group analysis.
Differences between experimental groups were analyzed by One way ANOVA (Bonferroni multiple group analysis test) for SB formation, clonogenicity and cisplatin sensitivity study using GraphPad PRISM software, version 3.0 (GraphPad Prism).
We fitted the model using multiple group analysis (Byrne, 2004).
*Kruskal Wallis Test for initial multiple group analysis; Mann Whitney U tests for two-group comparisons.
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