Sentence examples for generalized inferences from inspiring English sources

While the studies on participant and family satisfaction were too few to draw any strong generalized inferences, a contrast emerged between the NHSC and the US state programs.

One strength of this prospective population-based design, increasing our ability to draw generalized inferences, and despite not being a randomized controlled trial, is that the similarity between the groups at baseline provides a higher level of evidence as regards the effect of school transportation than previously published cross-sectional studies.

The population-based study design increase the ability to draw generalized inferences as do the fact that there were no differences in height, weight or body mass index (BMI) when comparing the study participants and the non-participants in the population based invited children [ 10, 11].

Instead of making generalized inferences according to the estimate from any single statistical model, results from the sensitivity analyses based on different models can provide some insight about the robustness of the findings.

Based on all simulation studies, a generalized inference confirms that it is difficult to judge upon the quality of the solutions obtained as far as their global optimality is concerned.

While this approach might be applicable for a discovery study with a small sample size (e.g. N = 3 pairs), it possesses no concept of statistical significance for generalized inference.

The WQS method allowed us to make generalized inference about the chemical mixture effect and identify the individual chemicals most strongly associated with NHL while considering the correlation between compounds.

Estimation of chemical weights and the resulting WQS index while considering the correlation between compounds allows us to make generalized inference about the mixture effect and identify the individual chemicals ("bad actors") most strongly associated with NHL.

(Generalization inference)   2.

The generalized Bayesian inference is based on the posterior distribution P{Θ| X, U, } of Θ given { X, U, Y = }.

In Section 6, by using the state space model in Section 5, we develop a generalized Bayesian inference procedure to estimate unknown parameters and to predict state variables.