Sentence examples for modeling diagnostics including from inspiring English sources

Modeling diagnostics, including Akaike's Information Criterion, over-dispersion, and influence graphs suggested the use of a negative binomial distribution to count for data over-dispersion.

We check the nature of the underlying model assumptions by performing model diagnostics including testing normality of residuals, homoscedasticity, plotting residuals against predicted outcomes, and comparing the actual experimental data to fake data generated from the estimated model [ 47, 48 ].

Model diagnostics, including studentized residuals and Cook's distance values, were inspected for outliers and highly influential points and models were evaluated for coherence with known emission source patterns and for sensitivity to alternative emission source indicators.

Other model diagnostics included checking the martingale residuals to detect non-linearity.

Multiple regression model diagnostics included residual analyses, multicollinearity analyses, and inferential analyses.

Model diagnostics included the calculation of Pearson residuals to identify outliers; observations with large residuals were further evaluated by re-fitting the model without the observation and comparing the coefficients to the full model.

20 The estimated models will be subject to usual diagnostics including goodness of fit and the level of internal prediction.

Regression diagnostics, including examining covariates for multicollinearity and model fit by R, were performed.

Standard diagnostics including residual plots and goodness of fit tests were used to validate the model fits.

We performed model diagnostic tests, including Hosmer-Lemeshow goodness of fit.

Diagnostics included color video and thin-filament pyrometry.