Sentence examples for analysis were selected in from inspiring English sources

For multivariate analysis, all variables that proved to be significant in the univariate analysis were selected in a stepwise fashion (forward selection of covariates) to evaluate the predictive power of each variable independently of the others.

Three microzones for the analysis were selected in the range of one coagulum (Fig. 2a).

The locations of the seismic stations used in the analysis were selected in order to focus specifically on the area near Unzen and the western edge of the Beppu-Shimabara Graben.

Plants for analysis were selected in phases of stem (32nd day of emergence), budding (40th day), and flowering (50th day).

Interventions for analysis were selected in consultation with the Ministry of Health in Mexico, based on policy priorities and ongoing debates regarding the content of packages of services in SSPH.

The sequences chosen for the analysis were selected in order to have representative candidates for all opsin classes (ciliary opsins [c-opsins], rhabdomeric opsins [r-opsins], Go-coupled opsins, neuropsins, peropsins and retinal G-protein coupled receptors [RGR opsin]; following [ 53– 53]).

Antibodies that produced a single or a dominating protein band at expected molecular weight in this analysis were selected for in situ PLA analysis to assure specificity of antibodies.

During logistic likelihood (log likelihood, Rwhole model = 0.1995) stepwise regression 6 factors for future analysis were selected: infections in continuous study, gender, age, wounds in PPS, physical activity and ulcers.

Variables with significant effects (p<0.10) in univariate analysis were selected for step-wise selection in multivariate regression.

Variables associated with cardiac index with a p value below 0.1 in univariate analysis were selected for inclusion in a multivariable mixed logistic regression model, using backward stepwise descending selection.

Covariates identified a priori (i.e., group, age category) and those with a p value <0.10 in bivariable analysis were selected for inclusion in multivariable models.