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The analysis of residuals plays an important role in validating the regression model.
An analysis of residuals verified accuracy of the model.
Assumptions of ANOVA were discussed and carefully examined using analysis of residuals.
Analysis of residuals shows possible heteroscedasticity and non-Gaussian tail behavior.
Verification of the model was accomplished through the analysis of residuals.
For this purpose, principal component analysis of residuals, fit statistics, and Rasch separation indices were examined.
Analysis of residuals, sensitivity analyses and analysis of joint confidence intervals provide an interesting tool for this purpose.
The reliability with which these parameters are derived is indicated by analysis of residuals, sensitivities and joint confidence intervals.
The resulting fit statistics and analysis of residuals (observed minus simulated PCE saturations) were used to improve the conceptual model.
In order to statistically assess the good specification of a model, we carried out an analysis of residuals.
Analysis of residuals using thresholds, faults tags, and logic charts enables on-line detection and isolation of faults.
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