Sentence examples for data assumptions from inspiring English sources

During the last 5 years, some data, assumptions, and models have been improved and altered significantly.

Moreover, indicators have the potential to be misleading, if the data, assumptions, or analyses behind them are incorrect.

This unexpected result was verified by Monte Carlo simulation of several different models, using both normally and lognormally distributed data assumptions.

Finally, two applied examples are used to demonstrate the sensitivity of inferences under different missing data assumptions and how this may change the substantive interpretation.

The data, assumptions, procedures, and results relevant to each component of the workflow are elaborated in much more detail in the following sections.

These advantages include, but are not limited to, the ability to incorporate time-varying predictors, handle dependence among repeated observations in a very flexible manner, and to provide accurate estimates with missing data under fairly unrestrictive missing data assumptions.

It is well known that this estimator is very sensitive to departures from the Gaussian data assumption.

In image processing with a short finite data, assumption of a power spectrum with known characteristics is generally not possible.

Third, the non-negative data assumption in Hozo et al.'s method is also quite restrictive.

Sensitivity analyses will be conducted to assess the robustness of the missing data assumption made in the primary analysis.

In this paper, we bring together earlier-derived yet perhaps now somewhat neglected results which show that a logistic regression complete records analysis can provide asymptotically unbiased estimates of the association of an exposure of interest with an outcome, adjusted for a number of confounders, under a surprisingly wide range of missing-data assumptions.