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Standard model diagnostic procedures adapted for multivariate models [14], [15] cannot be applied to survey data in Stata 8.0, therefore the adequacy of model fit and other diagnostic procedures such as assessing the normality and variance of residuals could not be assessed.
We will fit an unstructured covariance across time and employ necessary model diagnostic procedures [ 55].
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Thus, researchers need to have a clear understanding of the model, its parameterization and data considered, and knowledge of model diagnostics procedures and so on (Baty et al. 2015).
Routine model diagnostics procedures and goodness-of-fit were performed to check the adequacy of the logistic regression models.
To assess the goodness-of-fit of the statistical models, standard diagnostic procedures were performed.
We will employ the same model selection and diagnostic procedures with appropriate attention to the peculiarities of cost distributions (probability masses at zero, heteroskedasticity, skewness, and kurtosis).
We checked final models for the assumptions of linear regression and model fit using influence diagnostic procedures, examination of residuals, and residual residual plots.
For continuous variables, a multiple linear regression model will be used, and the same model-selection criteria and diagnostic procedures as described above will be applied.
Model 2 additionally includes diagnostic procedures from Table 3 entered as independent variables.
We describe breast cancer progression using a four-state partially observable Markov model and simultaneously estimate the transition rates between the states of the model and the sensitivity of diagnostic procedures.
Additionally, agreement between results of Cox models using data from simple diagnostic procedures and results from post-mortem analysis underline the potential use such diagnostic data to reduce calf mortality.
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