Sentence examples for using the selected data from inspiring English sources

The aim was to reduce the bias in using the selected data by ensuring that the variance component estimates reflected those that would be obtained if the full data was analysed.

However, the variance component for the spline term Z s u s in (3) was estimated using the selected data since the focus was on the variation among the 383 sires.

It also seems feasible that where limitations in sequence alignment algorithms prevent reliable placement of indels according to current phylogenetic-based guidelines in forensics, but consistent results are obtained using the selected data analysis pipeline, the variant calls for the relevant regions could be transformed bioinformatically prior to reporting.

It cannot be used on the selected data because the variable selection method (stepwise-CCA) was applied, changing the null distribution of the correlation between the outcomes and the exposures.

Using these settings, the selected data would be shown for all genes within the candidate interval.

Then, using specially developed software, the selected data was represented as animated glowing dots, combined with carefully mosaicked satellite images to accurately portray the movement of thousands of individuals, aircraft, buses, trains, ferries, and subway trains across the very landscape where the data was originally generated.

Sensitivity analyses were then used to assess the impact of the selected data transformation method (using prespecified log-normal regression analysis) and the impact of outliers (using prespecified zero-truncated negative binomial regression analysis) on the LOS.

These values (, and the values in Table 1) were fixed in the analysis of the selected data set using only the daughters of the 383 sires and the same mixed model.

The statistics collected on the selected data are used to perform the noise reduction on the whole image.

Once the features are identified, validation and test accuracies are computed using a standard (non-sparse) SVM built from the training data using the selected features, and evaluated on the validation or test data.

This parameter was chosen such that the resulted data using the selected parameter's value show some sort of similarity (in its overall representation) to the pattern of the original data to be downwardly continued.