Sentence examples for multivariate outliers are from inspiring English sources

Univariate or multivariate outliers are important because they change the results of data analysis.

Multivariate outliers are usually detected based on robust distance which uses the robust estimate of mean and covariance in the calculation of Mahalanobis Distance [ 17].

After univariate and multivariate outliers are examined, demographics and outcome scores prior to the intervention will be examined to investigate the equivalency of the group characteristics between the two groups.

Both univariate and multivariate outliers were removed from analysis by applying these tests, and the resulting sample size is reported separately for each MANOVA analysis.

Multivariate outliers were screened separately for each planned analysis by conducting a linear regression with participant ID as the independent variable (IV) and all primary outcome dependent variables (DVs) for the planned analysis as the regression DVs.

Even though the easiest way to detect the multivariate outliers is multidimensional scatter plot, some methods based on the Mahalanobis distance or Cooks distance have been suggested in the literature.

Multivariate outliers were identified using Mahalanobis distance [ 35, 36].

Multivariate outliers were identified using standardized residual and Mahalanobis distance.

The multivariate outliers were screened using the Mahalanobis distance scores for all cases (D2).

Finally, the presence of multivariate outliers was tested with Mahalanobis's distance to the mean.

Six multivariate outliers were found and deleted prior to model testing.