Examples of using Distributed variables in English and their translations into Portuguese
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Colloquial
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Official
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Medicine
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Financial
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Ecclesiastic
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Ecclesiastic
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Computer
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Official/political
Normally distributed variables are presented as mean± standard deviation.
To compare the means,the T test was used for normally distributed variables.
For the scores and non-normally distributed variables, comparison between groups was done by the Mann-Whitney U and the Kruskal Wallis test.
Mean and median values andstandard deviations were used to examine normally distributed variables.
Continuous normally distributed variables determined by Kolmogorov-Smirnov test were compared using ANOVA and reported as means standard error.
Independent samples test was used to compare normally distributed variables between groups.
Student's t tests for normally distributed variables and Mann-Whitney test for nonparametric variables were used for the statistical analysis.
Means were compared using one-way analysis of variance orthe Kruskal-Wallis test for non normal distributed variables.
The Student's t test was used to compare normally distributed variables between groups of patients who developed PE and those who did not develop PE.
Regarding the quantitative variables, Student's t-test was used to compare the means of normally distributed variables.
The descriptive data are presented as percentages,means SD for normally distributed variables and medians IQR for non-normally continuous variables. .
A two-tailed unpaired Student's t-test was performed to identify significant between-group differences in normally distributed variables.
For intergroup comparison,the paired Student t test was used for normally distributed variables and Wilcoxon matched pairs test for the other.
For parameters presenting a non-Gaussian distribution,values were log-normalized prior to use in tests that require normally distributed variables.
By the sample calculation of normally distributed variables, using an alpha error of 95% and a beta error of 80%, it was concluded that a minimum of 9 individuals should be recruited in each group.
Differences between before and after PCLM times were evaluated using Wilcoxon,Mann-Whitney U tests comparing not normally distributed variables.
Analysis of variance one-way ANOVA was used to analyze the mean values of normally distributed variables in the three study groups, assuming equal variances between groups.
Parametrically distributed variables were expressed as means± standard deviations, and non-parametrically distributed variables were expressed as medians and interquartile ranges.
For continuous variables, the independent-groups t test was used for normally distributed variables or the nonparametric Mann-Whitney U test, if the normality assumption was violated.
Normally distributed variables were expressed as means± SD unless noted otherwise, and non-normally distributed variables were expressed as either medians and ranges or percentages.
For quantitative variables, we used the Student t-test to compare the means of normally distributed variables or the Mann-Whitney test to compare the interquartile distributions.
Data are shown as mean andstandard deviation for normally distributed variables or as medians and interquartile ranges for quantitative variables with non-normal distribution.
Descriptive statistics, such as mean± standard deviation for normally distributed data or median andinterquartile range IQR for non-normally distributed variables, were calculated for quantitative parameters.
For the comparative analyses, the paired orindependent Student's t tests were applied for normally distributed variables, and the Mann-Whitney or Wilcoxon tests were used for non-normally distributed variables. .
The following statistical tests were applied: the Student's t-test for independentsamples to compare groups; repeated-measures t-test to compare pre- and post-training time points for normally distributed variables; and the Mann-Whitney test for non-normally distributed variables.
Comparison between groups(HFA and non-HFA)was performed using T-Test for normally distributed variables, Wilcoxon test for non-normally distributed variables and x 2 test for categorical variables. .
The correlation coefficient r between clinical, anthropometric, biochemical data, PWV andcarotid parameters was obtained by use of the Pearson method for normally distributed variables, and the Spearman method for nonparametric distributed variables.
Continuous variables with normal distribution were compared using t-tests, while non-normally distributed variables were compared using the Mann-Whitney non-parametric test.
To evaluate the changes in continuous variables in relation to baseline, the paired Student's t test was used for normally distributed variables or paired Wilcoxon test if the distribution was not normal.
Regarding the continuous data, the median and the 25- 75% interquartile interval Q1- Q3 were used forthe non-normally distributed variables, whereas the mean and standard deviation SD were used for the normally distributed variables.