Exemplos de uso de Normality assumption em Inglês e suas traduções para o Português
{-}
-
Colloquial
-
Official
-
Medicine
-
Financial
-
Ecclesiastic
-
Ecclesiastic
-
Computer
-
Official/political
Furthermore, GEE model does not require normality assumption.
The normality assumption of the data was verified by means of the Shapiro-Wilk test.
Messenger RNA values were log-transformed to conform to normality assumptions.
When the normality assumption was rejected, the nonparametric Mann-Whitney test was used.
In these cases, the results of an analysis based on normality assumption may be unsatisfactory.
As pessoas também se traduzem
When the normality assumption was violated, the Kruskal-Wallis test was used with Mann-Whitney post-tests.
In general, the probability distribution of positive responses is asymmetrical,invalidating the normality assumption.
All parameters were evaluated by the shapiro-wilk normality assumption and the samples subjected to anova and post-hoc tukey test p.
The normality assumption was evaluated by visual inspection of histograms and D'Agostino-Pearson omnibus normality test.
Friedman's nonparametric test was used to assess group behavior, since the normality assumption was rejected.
Under normality assumptions about error distributions, the counterpart of the sum of squares due to error has a Wishart distribution.
This is an important statistical character because hypotheses tests as well as other modeling techniques require the normality assumption.
The normality assumption was rejected by the Kolmogorov-Smirnov test and, therefore, these variables were transformed by means of the logarithmic function.
For comparison between the GM and GN groups,we used the Student t test for independent samples, as the normality assumption was granted for groups.
Moreover, the normality assumption of the residues was met as required by the central limit theorem in view of the large number of firms in the sample.
For continuous variables, the independent-groups t test was used for normally distributed variables orthe nonparametric Mann-Whitney U test, if the normality assumption was violated.
The univariate and multivariate normality assumptions of the items were evaluated according to the asymmetry(sk) and kurtosis(ku) coefficients.
The evaluation of the parameters was performed using the Spearman correlation test to perform non-parametric correlation, ie,it was assumed the normality assumption of the values studied.
After finding the normality assumption, we followed up with the comparison of data using the ANOVA two-way test repeated measures to the RMS EMGn variables.
The following statistical tests were used: 1 Student's t-test, for the comparison between groups,2 Kolmogorov-Smirnov test to verify the validity of the normality assumption, and 3 Wilcoxon and Mann-Whitney tests, when the assumption of normality was not valid.
When the normality assumption was confirmed, Student's t test and variance analysis ANOVA were used to compare the continuous measurements between two and among three or more groups, respectively.
Mean indicators were developed to treat the data, which were subjected to variance analysis ANOVA andto Tukey multiple-comparison test whenever the normality assumptions were met in the Kolmogorov-Smirnov test, the Lilliefors and homogeneous variance correction were performed through Levene test.
The normality assumption of the data was investigated using the Kolmogorov-Smirnov test, which revealed significant deviations from normality in some of the variables, as presented below, in Table 1.
The T-Student test was used to check for differences between the sway velocity mean values and the ellipse area in the Balance Rehabilitation Unit BRU situation between the BPPV experimental group andthe control group, because the normality assumption was rejected by the Kolmogorov-Smirnov test, and these variables were transformed by means of the logarithmical function.
Residual analysis did not show any violation of the normality assumption, and the residuals show a symmetrical distribution around zero, with an approximately constant variance.
As normality assumptions were not met, non-parametric analysis techniques Mann-Whitney and Kruskal-Wallis tests were applied to locate the differences between groups, according to the hypotheses suggested by this study.
In relation to normality, the results from the Kolmogorov-Smirnov test reveal that the normality assumption is fulfilled, given that the sig values are 0.926 and 0.713 in the organizational and operational complexity dimensions, respectively; in other words, the data follow normal distribution.
The validity of the normality assumption of error terms distribution is confirmed with the graphic of normal probability for residues and the Kolmogorov-Smirnov test of adherence to normality, corrected with Lilliefors or Shapiro-Wilks.
The standard method of constructing confidence intervals for linear regression coefficients relies on the normality assumption, which is justified if either: the errors in the regression are normally distributed(the so-called classic regression assumption), or the number of observations n is sufficiently large, in which case the estimator is approximately normally distributed.
The class of models glmm extends the normality assumption of the data and allows the use of several other probability distributions, for example, accommodating the over dispersion often observed and also the correlation among observations in longitudinal or repeated measures studies.