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ANOVA test was used to analyze the differences among IC50 of various fractions for different antioxidant assays, with least significance difference (LSD) P < 0.01 as a level of significance.
Student t test was used to analyze the differences among continuous variables.
To further analyze the differences among our approaches, Table 7 presents the top 10 most frequent features extracted with each technique in both datasets used in our experiments.
Analysis of variance (ANOVA), developed by Fisher (1932), is the most accepted statistical methods used to analyze the differences among group means.
Analysis of variance (ANOVA) is a set consists of a number of statistical methods used to analyze the differences among group means and their associated procedures.
To analyze the differences among groups according to CXCL9-11 genotypes, Mann–Whitney U test and Kruskal Wallis were used for continuous variables and Chi square test or Fisher's exact test for categorical variables.
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ANOVA test was used to analyze the difference among various treatments with least significance difference (LSD) at 0.05 followed by Bonferroni's posttest.
One-way ANOVA was used to analyze the difference among groups.
The Mann–Whitney U-test and the Kruskal-Wallis test were used to analyze the difference among groups.
The data of A570 value and relative gene expression value were expressed as the mean ± S.D.. Duncan's multiple range tests were used to analyze the difference among groups with the software SPSS 20.0.
Data were expressed as mean and standard error (SE) and ANOVA test were used to analyze the difference among various treatments, with least significance difference (LSD) at 0.05 and 0.01 as a level of significance.
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