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Significant difference between each two different groups of data was examined using the Student's t test.
It allowed users to visually connect different groups of data or activities and updates in different visualizations for a visual analytics task, particularly using color-coded search keywords.
The Student's t-test and the Pearson's chi-squared test were used to compare different groups of data.
However, two G-ending codons, AGG (arginine) and TTG (leucine), defy this overall trend consistently across the three different groups of data by showing a negative correlation between usage and GC bias.
Different groups of data were analysed with appropriate statistical methods.
Constant comparison strategies were used by systematic examination of similarities between the different groups of data.
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DSS intake did not differ between the different groups of mice (data not shown).
The association between methylation levels with breast cancer risk, however, did not differ according to the source of the control (data not shown), and methylation levels did not vary between the different groups of controls (data not shown).
No statistically significant differences were found in these groups regarding diabetes, MetS, and different groups of BMI (data not shown).
Analysis of variance (ANOVA) for the single factor was used to determine if the means of different groups of the data were equal.
It is possible to enumerate two main problems in data stream clustering which are concept change and visiting data once and there are different groups of solutions for data stream clustering from different perspectives that we summarized them in Table 1.
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