Sentence examples for minimize statistical errors from inspiring English sources

A full factorial design is used to minimize statistical errors and study interactions accurately.

Once the initial transient has subsided, global diagnostic quantities (Λ i, Rm, ℓ B, Λ d, and (Lambda _{d}^)) are time-averaged to minimize statistical errors.

The track counting was performed in 20 views for each detector, which were swept over the detector surface, with at least 50 counts per view to minimize statistical errors and to obtain the tracks density ρ x (tracks/cm2).

Traditional engineering type of models aim to minimize statistical errors, however, the model with minimum engineering type of statistical errors does not necessarily guarantee maximized trading profits, which is often deemed as the ultimate objective of financial application.

For the evaluation of the density of alpha tracks,ρ x, on the detector surface, the tracks were counted using a binocular microscope, where 20 views were taken for each detector with counts above 50 in each view in order to minimize statistical errors.

Only segments with at least 600 emitters were analyzed to minimize statistical error.

The system includes eight large NaI(Tl) detectors that measure adjacent intervals of the core simultaneously, maximizing counting times and minimizing statistical error for the limited measurement times available during drilling expeditions.

To minimize the statistical error, each acquisition has been made 10 times and the averages of these 10 acquisitions are used in the analysis.

To minimize possible statistical error caused by a paucity of data, we employed pseudo-counts for n i x and n i, j x, y.

We performed three replicates of the above sampling and annotation procedure with Transposome for each species to minimize the statistical error in our estimates of genome composition.

Reliable methods for predicting delta during study design and better reporting of the basis for these predictions are needed to minimize the risk of trial failure from type II statistical errors and resulting waste of research resources.