Exemplos de uso de Normality and homoscedasticity em Inglês e suas traduções para o Português
{-}
-
Colloquial
-
Official
-
Medicine
-
Financial
-
Ecclesiastic
-
Ecclesiastic
-
Computer
-
Official/political
The data obtained for each material attended the assumptions of normality and homoscedasticity and a two-way anova =5.
The normality and homoscedasticity test results of the residues of each model are shown in Table 1.
The distribution of data was evaluated as for the normality and homoscedasticity, and the linear trend was determined.
Data normality and homoscedasticity were verified by the Shapiro-Wilk and Levene tests, respectively.
The questionnaire was tested in terms of internal consistency, normality, and homoscedasticity to determine which was the best fit to the data.
The normality and homoscedasticity of the data were analyzed using the Shapiro-Wilksand Levene's tests, respectively.
Following the evaluation of the multiple regression analysis, the normality and homoscedasticity of the residuals were established using Q-Q plots.
Initially, normality and homoscedasticity tests were used to observe distributionand its homogeneity.
Such test is proposed for independent samples, in cases where there is a rupture of parametric assumptions,particularly those regarding normality and homoscedasticity assumption of constant variance across subsets of data.
The data were tested for normality and homoscedasticity by the shapiro-wilk and levene's test, respectively.
However, for the results to be reliable, it is necessary to check the assumptions of anova,which are additivity of effects in the model and inde- pendence, normality and homoscedasticity of errors.
In order to assess normality and homoscedasticity, the Shapiro Wilk and Levene tests were performed, respectively.
In order to compare MML-PM and the categorical variables of gender, laterality,THI and BDI we carried out the t-student test with habitual assumptions of the model normality and homoscedasticity and 2 groups were compared, otherwise the Mann-Whitney test was employed.
To assess normality and homoscedasticity of the data, the Kolmogorov-Smirnov and Levene test were utilized, respectively.
Statistics and data analysis procedure- Initially, normality and homoscedasticity of distribution were tested, validating the use of parametric statistics.
Firstly, the normality and homoscedasticity presuppositions were tested which presented themselves trueand applied the de durbin-watson test which pointed out first order residual autocorrelation(¿_1), which was considered i.
We should mention that such a test was used when normality and homoscedasticity criteria of variance were met, which were respectively verified by the Shapiro-Wilk and the Levene tests.
For verification of normality and homoscedasticity of data the Liliefors and Cochran& Bartlett tests were used, respectively.
Initially, we performed normality and homoscedasticity tests in order to analyze data distribution and homogeneity.
Not satisfied the assumption of normality and homoscedasticity by Shapiro-Wilk and Levene's tests, the Kruskal-Wallis test was performed.
For the bivariate analysis of the data, normality and homoscedasticity of data using the Kolmogorov-Smirnov testand Levene test were evaluated, respectively.
If the assumptions for normality and homoscedasticity were not satisfied using the Shapiro-Wilkand Levene tests, the Kruskal-Wallis test was applied with a post-hoc Dunn.
Due to the presence of normality and homoscedasticity after radical change in MIPand MEP variables, their analysis were performed by a completely randomized design in a split plot system Split-Plot.
When detected normality and homoscedasticity, data were analyzed by analysis of variance ANOVAand means were compared by Tukey's test at 5% significance Barbin, 2003.
Initially, we tested the normality Kolmogorov-Smirnov and homoscedasticity of the distribution Hartley test, validating the use of parametric statistics.