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Drug effect was evaluated on all the disease progression model parts: the expected number of CELs (λ(t)), the baseline expected number of CELs (λ0 t)), the overdispersion (θOVDP × OVDP), the first order Markovian effect (θPDV × PDV), and the second order Markovian effect (θPPDV × PPDV; Figure 1 d ).
The set of CNSs allowed us to establish a baseline expected number of HARs or haCNSs nearby PFO vs. PT genes if there were no association between accelerated evolution and differential expression.
An inhibitory effect on the baseline expected number of CELs (λ0 t)) best described the data.
The population analysis performed here identified the inhibitory effect on the baseline expected number of CELs (λ0 t)).
1 Here, λ(t) is a function of the baseline expected number of CELs [λ0 t)], the observation in the previous month DVt-1 (previous dependent variable [PDV]) and the observation two months before DVt-2 (previous previous dependent variable [PPDV]; Eq. 2). 2 This disease progression model defines the baseline expected number of CELs [λ0 t)] as a constant θλ0 (Table 1; model M0).
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Systematic methods for determining the expected number of visits on a particular day require historical data to create baselines in trimmed-mean seasonal and autoregressive integrated moving average (ARIMA) models.
The building's collapse risk, expected annual losses and the expected number of casualties are calculated.
Expected number of failures.
expected number of crashes.
Data represent expected number of cases (prevalence).
Whether these numbers significantly exceeded the expected numbers was determined looking at the upper 95% confidence level for the expected number assuming a Poisson distribution for the variance of the expected baseline number, a continuity correction, and the exact value for the Poisson distribution function in Excel 2000 [ 6].
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