Sentence examples for population benchmark from inspiring English sources

To control for potential differences in age and gender distributions in the two samples, the US general population benchmark sample was matched to the age and gender of the acute phase sample using separate least squares multiple regression models for each SF-12v2 domain and both PCS and MCS scores.

As a control, a gene list sampling equal to Tier 1 in size and selected from the T-test enriched population (benchmark method, no CDM processing) was analyzed.

Lymphoma and Non-Acute Leukaemia patients (AUC 0.90), and malignant breast disorders (AUC 0.90) improved substantially, and patients with laryngoscopic, mediastinal and other chest procedures surpassed the general population benchmark (AUC 0.94).

As shown, a larger coefficient of variation of weights for the EWP sample indicates that more adjustment is needed for the EWP sample to match the population benchmark than for the LA-RDD sample.

These survey-quality and design-efficiency measures include: coefficients of variation in weights, where a larger coefficient of variation in weights means more extreme adjustment is needed for the sample to match the population benchmark; design effects, which are ratios of variance of estimates of a sampling method compared with a simple random sampling design.

In the future our benchmark curves may be replaced by population benchmarks of functional disability [ 19]; until such a time, our benchmarks will prove useful.

Calculation of weights, in an overlapping dual-frame design, ideally requires type of phone use benchmarks as well as population benchmarks [ 7].

The work here provides the first national population benchmarks of some aspects of early childhood development as this pertains to readiness to learn at school.

The survey sample was broadly representative of the population of Aboriginal children living in Western Australia, although comparisons with population benchmarks revealed that age, household size and region were significantly associated with non-response.

The probability of selection of the i th person in the j th household is the product of these two probabilities and so the corresponding weight is: w ijh = π ijh − 1 = T h t h N jh T jh Adjust the weights to agree with externally derived population benchmarks, N dh.

Because weights are used to eliminate bias that would arise from ignoring the differences in selection probabilities and also to improve estimates by adjusting to known population benchmarks, when a design change occurs it is also important to assess how the design effect changes due to weighting, using weighting effects.