Introduction
Model diagnostics evaluate statistical model performance, assumptions, and reliability through residual analysis, goodness-of-fit tests, and validation procedures. These diagnostics encompass assumption checking, outlier detection, and model comparison techniques essential for robust statistical analysis. Explore expert-level tutorials, tools, and abstracts addressing model diagnostics implementation, validation strategies, and statistical quality assurance.
