Examples of using Nonparametric data in English and their translations into Portuguese
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Ecclesiastic
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Computer
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Official/political
Spearman's correlation tested the association of nonparametric data.
Nonparametric data were expressed as median and quartiles P25-P75.
The analysis of variance ANOVA for nonparametric data was used to assess the variables.
Nonparametric data are presented as medians and quartiles P25-P75.
Parametric data were expressed as means andstandard deviations; nonparametric data as medians and percentiles.
Nonparametric data were represented by the total number and percentage.
Since the variables did not show normal distribution,they were compared by using the Wilcoxon test for nonparametric data.
Nonparametric data were expressed as median/minimum/maximum value, and the Mann-Whitney test was used.
We used the Student's t-test to analyze the parametric data andthe Mann-Whitney U-test for the nonparametric data.
The comparison of nonparametric data between the groups was performed with the Pearson's Chi-square analysis.
From the Shapiro-Wilk test,it was found that some variables presented nonparametric data distribution 6 out of 19.
Nonparametric data were compared using Fisher's exact test, and parametric data were compared using Student's t-test.
The results were analyzed by t test orone-way ANOVA for parametric data; and Mann-Whitney test for the nonparametric data.
The nonparametric data were expressed as median and interquartile range and the groups were compared using Kruskal-Wallis test.
Comparisons between the medians were performed using the Mann-Whitney,after finding the characteristics nonparametric data.
For comparison among groups used the Kruskal-Wallis test for nonparametric data degree of visibility; significance level was 5% or p=0.05.
Numerical variables were compared through Student's t-test and the Mann-Whitney U test,the latter being used to treat nonparametric data.
The Kruskal-Wallis H andMann-Whitney U tests were used for comparison of nonparametric data from more than two groups and two groups respectively.
All samples were evaluated by Fisher's exact test for parametric data andKruskal-Wallis test for nonparametric data.
Parametric data are shown as mean± standard deviation, whereas nonparametric data are presented as median 25% percentile, 75% percentile.
Statistical analysis was performed using Student's t test for parametric data andthe chi-square test for nonparametric data.
The quantitative variables were expressed as medians for nonparametric data, or as means for parametric data, and coefficient of variation CV.
Correlation analysis between continuous variables will be made by the Pearson parametric data or Spearman nonparametric data tests.
Fisher's exact test was used to analyze nonparametric data. The chi-square test was used for parametric data and analyses of mean values and standard deviations.
Descriptive data are shown as mean± standard deviation SD for parametric data ormedian interquartile range[IQR] for nonparametric data.
The method was a nonparametric data approach that was aimed at assessing whether two independent samples are from the same population and especially designed for small samples.
All samples were evaluated using the Student t, chi-square, Friedman and Wilcoxon tests for parametric data andthe Kruskal-Wallis test for nonparametric data.
According to the data distribution, the Student's t-test parametric data andthe U Mann Whitney test nonparametric data were used for comparing the parameters between the groups.
The strength measurements, anthropometric variables andages were compared between groups using a Student's t test for parametric data and a Mann-Whitney test for nonparametric data.
Normality of the data was evaluated by the Kolmogorov-Smirnov test, andthe data are expressed as medians nonparametric data or as means and respective confidence intervals parametric data. .
