Sentence examples for statistical approaches were used from inspiring English sources

Different statistical approaches were used for detecting statistically significant genes, depending on the study design used in each cohort (Table 3).

Multivariate statistical approaches were used to reveal the relationships between soil properties, metals concentrations in soil, and their bioaccumulation in vegetables.

The results from mixture experiments were analyzed to predict joint effects according to concentration addition and statistical approaches were used to characterize the potential interactions between the components of the mixtures (synergism/antagonism).

To detect changes in water quality in response to PWS schemes, nonparametric statistical approaches were used to analyze gradual and abrupt trends in water quality, focusing on chemical oxygen demand (COD) and ammonia-nitrogen (NH3-N) at 26 monitoring stations in the Huai River watershed during 2006 2013.

Stepwise multiple linear regression and decision tree statistical approaches were used to assess the relationship between the land cover predictors and benthic indices of biological integrity (BIBI) and number of sensitive invertebrate taxa (NEPT), response variables derived from the Maryland Biological Stream Survey MBSSS).

Two statistical approaches were used: a Factorial Correspondence Analysis (FCA) and non-parametric statistical tests.

In most applications of geographical analyses, statistical approaches are used in a "cookbook" fashion without considering the spatial features of the data (Fotheringham et al. 2000).

A range of statistical approaches is used here to reconstruct the spatial distribution of AGB found in a tropical dry forest in Mexico.

Predominantly, uni-variate statistical approaches are used, the employed optimization methods are computationally costly, and the design process is rather opaque and lacks interactivity options for the user during the design process.

In estimation theory, statistical approaches are used to estimate the state of a dynamical system by combining all available knowledge pertaining to the system including the measurements and the modeling theories.

If the underlying network of interactions is sparse, two main statistical approaches are used to retrieve such a structure: covariance modeling approaches with a penalty constraint that encourages sparsity of the network, and nodewise regression approaches with sparse regression methods applied at each node.