Sentence examples for method variables were from inspiring English sources

Using a Forward Stepwise Likelihood ratio selection method, variables were tested for significance.

We then searched for possible confounding variables using multivariate analysis and a forward stepwise method; variables were entered into the equation if the significance of the association on univariate analysis, P, was <0.1.

Because of the data reduction approach used for index construction (principal components analysis (PCA), discussed in detail below), and the statistical assumptions implied by this method, variables were assessed for normality.

In the stepwise method, variables are added one by one to the model and the F statistic for a variable to be added must be significant at the SLENTRY = level.

In this method, variables are successively added to the model based on the higher F to enter values; variable addition ceases when the F-ratio is no longer significant.

Through the detailed recording methods, all variables were at least 95% complete.

By use of graphical methods, continuous variables were checked for normality and are expressed as mean ± SD.

For the comparisons between the four methods, continuous variables were analyzed using a mixed model after square-root transformation of the variables that were not normally distributed.

In keeping with previous methods, neuropathological variables were dichotomized into 'high' or 'low' values (Savva et al., 2009; Brayne et al., 2010).

Interactions between feeding method and infant variables were not significant.

Survival was calculated by the Kaplan-Meier method, differences between variables were estimated by the Log-rank test.