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One-way analysis of variance (ANOVA) was employed to assess the mean difference among the groups compared to control.
Analysis of variance was used to test mean difference among continuous variables.
The y-axis is the power and the x-axis is the mean difference among the Paini measurements (e.g., Pain2 - Pain1).
We employed Kruskal-Wallis test to determine whether there was any mean difference among groups in the scenario with three-category outcome variable.
Only items with a significant mean difference among users served as primary dependent variables, which were Items 2 (privacy protection by patient consent) and 3 (supporting the promotion), for further multivariate analysis.
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Building on Lee's [3] work, the present study focuses on analysis of mean differences among different healthcare user groups and apply the results to improve care quality specific to different treatment experiences (e.g., inpatient, outpatient and emergency).
In MANOVA test, the null hypothesis is rejected if one or more of the mean differences or some combination of mean differences among the genes in gene set differs from zero.
The significant changes within the group (pre- and post-treatment/post-follow-up values) were determined by paired 't'-test and the mean differences among the groups were compared by Student 't'-test.
Mean differences among multilocus genotypes ranged 12.1 20.7% of the markers among coastal populations and 9.0 14.1% among montane populations.
One-way ANOVA was performed for each metabolite to find the mean differences among the four STRUCTURE subpopulations.
Χ2 tests were used to determine differences in frequencies across categories, and linear regression with categorical variables was used to assess mean differences among milk or dairy categories.
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