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Open image in new window Fig. 13 Two-year points for daily electricity use per gross floor area and cooling 4P regression model for building E5, in Campus 1. Figure 14 shows an example of 5P regression model adjusting the daily electricity consumption as a function of daily average external temperature.
In the generic model, adjusting the positive feedback gain can result in infinite amplification [ 10, 12, 49].
In the second model, adjusting the analyses for gender, ethnic background, age and marital status, all associations between substance use and group membership became somewhat weaker (except for daily use of tobacco in SEL), though remaining significant.
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The model adjusts the half-life to the actual daily temperature of the scenario.
The model adjusted the component properties to match the observed fluid properties.
The second model adjusted the first with the incorporation of demographic characteristics as well as MIDAS disability, comorbidity count, and disease duration covariates.
This model adjusts the survival analysis conditional on the propensity score.
Thus, each model adjusts the probability of health status transition for the competing risks of death and being LTF.
These two views are then combined by various decision models adjusting the original estimate up or down, and often with confidence intervals and factors of safety.
Interestingly, all mixture PD models adjusted the group differences provided by the 2PLM to a similar extent.
The models adjusted the parameter estimates for the clustering and unequal survey weights within NHANES.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com