Sentence examples for model expectation for from inspiring English sources

The calculated mean value of detection time and its standard error was 2.81 ± 0.02 months (τ = 4.26 yrs−1), while its median was 2 with an interquartile-range also of 2. Given these parameters, the model expectation for the flow into the under-treatment class T of a cohort I0 is (4) I 0 → T t : = I 0 ⋅ 1 − e − τκ ⋅ t where I0 = 16,257, τ = 4.26 yrs−1 and κ = 0.87.

The calculated mean value of treatment length and its standard error was 8.80 ± 0.03 months (δ T  = 1.36 yrs−1), while its median was 9 with an interquartile-range of 4. As before, the model expectation for the flow out of the under-treatment class T of a cohort T0 becomes (5) T 0 → I t + L t : = T 0 ⋅ 1 − e − δ T ⋅ t where T0 = 17,477.

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Generally non-significant deviations from the model expectations are expected for chi-square-based statistics, and within range (± 2.5) for residual fit statistics.

This is confirmed by a series of fit statistics, where Chi-Square based statistics are shown to be non-significant (i.e. no deviation from model expectation) after adjustment for multiple testing [ 21].

This normalized distance then represents the relative excess or deficit of contacts observed compared with the model expectation and controls for both proximity along the chromosome and broad-scale chromatin context.

Many items deleted because of misfit with model expectations showed considerable bias for gender.

Because the initial fit of the racked data produced a significant item-trait interaction with several misfitting items and persons, an attempt at transforming the DASH was made to meet the Rasch model expectations and thus allowing for interpretation of the results.

These results are not consistent with the neutral model expectations and suggest a role for natural selection to maintain high frequencies of certain amino acids.

It is also important to note that unidimensionality is a necessary, but not sufficient, condition for satisfying Rasch model expectations, as the model imposes extra constraints on the data to satisfy the rules for constructing interval scale data [ 46].

The Physical, Cognitive, and Summary scales all demonstrated fit to model expectations, with ordered thresholds, no DIF for person factors, no local dependency and strict unidimensionality (Table 4, analyses 10-12).