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Although these studies are heterogeneous in terms of design, intervention methods, and measures of outcome, bias can be reduced using statistical analysis strategies.
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Data were reduced using HKL2000.
The substantial computation time may be reduced by using statistical learning techniques to develop surrogate models, or metamodels, which efficiently approximate the complex and implicit relationship between predictor variables, such as bridge design and ground motion intensity parameters, and the predicted bridge component seismic responses (e.g., column and bearing deformations).
Color crosstalk in STORM images can be reduced by using a statistical analysis equivalent to linear un-mixing in conventional fluorescence images, as described previously.
The discrepancy in the results could be reduced by using a more accurate wave function and by taking the MD sampling to reduce statistical errors.
The size of feature vectors was reduced by using five statistical parameters to get rid of the computing load.
Data were integrated and reduced using HKL2000 [21].
The wide number of parameters that can influence dust source term production is reduced with statistical tools using a combination of screening, sensitivity analysis, and uncertainty analysis.
If (p_{k}=1) for all k then weighted statistical convergence is reduced to statistical convergence.
Data were reduced and scaled using HKL2000 [22].
All statistical analyses were performed using statistical software (Statistica, StatSoft).
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