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However, imputations for missing data are model-dependent draws from the posterior distribution of random variables, conditional on the observed values of other available variables, and requiring use of estimated relationships between the variable that is missing and the remainder of the variables.
Let us now demonstrate how the data are modeled according to this model.
In general, the degradation data are modeled by a nonlinear regression model with random coefficients.
The thermal conductivity data are modelled by an approach similar to the well known Krischer model.
The model has been formulated as a multi objective linear programming model where data are modeled by triangular fuzzy numbers.
Modified ITT population; data are model-adjusted mean (95% CI), or number At 12 months, the reduction in 24-hour sBP was significantly greater (p = 0.016) for rosiglitazone added to metformin (-4.9 mmHg) than for sulfonylurea added to metformin (-2.2 mmHg).
In linear regression, data are modeled using linear predictor functions, while unknown model parameters are estimated from the data.
In LRAcluster probabilistic model, the real-type data are modeled as Gaussian-distributed random variables with variance 1.
Data were modelled using the Lambert Pearson Model, a mathematical modelling approach previously used for bacterial inhibition.
Given EEG data is modeled as the output of stable AR system. 3.
The survey data was modeled using analysis of variance (ANOVA and ANCOVAANOVA and ANCOVA
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