Sentence examples for data worst from inspiring English sources

For this scenario, the models with the best fit were SD and AM, followed by Models AS and S. The models that fitted the simulated data worst were A and AMS.> -wrap-foot> σ a 2 and σ m 2 are the direct and maternal additive genetic variances and σ am is the direct-maternal genetic covariance.

It was closely followed by Models AM and SD, whereas Models AS, S and A fitted the simulated data worst.> -wrap-foot> σ a 2 and σ m 2 are the direct and maternal additive genetic variances and σ am is the direct-maternal genetic covariance.

Meanwhile, dreadful January BBA mortgage data, the worst for two years, offers a sobering wake-up call.

If researchers are basing their hypotheses on published literature alone, they will likely be wasting time and money on repeating failed experiments – at best, reproducing false data; at worst, creating a new set of false positives.

See also: Metadata: Data Security’s Worst Year Yet.

that is, select the allocation that minimizes the maximum Euclidean distance to the data symbols (worst symbol) in the preestimated symbol distribution.

In all cases, the RT data performs worst which is expected due to the inferior temporal resolution, with STD values sometimes twice as large compared to the other solutions.

Consequently, even though older patients' response to HAART is parallel to younger patients' response, their immune and virological data were worst at first clinic visit and thus their progression to AIDS was faster and their survival was shorter.

Chiasson reviewed the evidence of prevention of cardiovascular disease outcomes in the study (56), characterizing it "at best [as] hypothesis-generating data, at worst … a fluke, so let's not over-interpret".

For study-specific reference panels that included WES data, the worst performing reference panel had the smallest number of overlapping variants, while the best performing reference panel had the most overlapping variants.

The application of restriction-based protocols at taxonomic levels beyond that of populations, or perhaps subspecific taxa, entails a greater risk due to the possibility that many restriction sites may possess mutations in a subset of the study samples, resulting in frequent allele dropout and, at worst, data matrices consisting largely of missing data.