Residuanalys

Residuanalys is a term used in statistical discourse to denote a systematic approach to residual analysis, the examination of the differences between observed data and model predictions. The term is not standardized everywhere, but is used in some texts and software references as a concise label for diagnostic work on residuals.

Practically, residuanalys involves generating residuals from a fitted model and applying graphical and numerical checks to assess assumptions and fit. Common tools include residual plots (residuals versus fitted values), standardized and studentized residuals, and Q‑Q plots to gauge normality. Additional diagnostics look at leverage and influence, using measures such as Cook's distance and DFBETAs, as well as tests for heteroscedasticity and autocorrelation. Transformations of the response or predictor terms, or alternative models, are often considered when patterns or nonconstancy of variance emerge.

Residuanalys applies across modeling contexts, including ordinary least squares regression, generalized linear models, and time-series or mixed-effects models. It guides model refinement by revealing misspecification, omitted variables, nonlinearity, or unusual data points that distort conclusions.

Interpreting residuanalys findings requires understanding that residual patterns do not prove causality; they indicate the need for model revision or alternative methods. It is best used in combination with cross-validation and domain knowledge to ensure robust inference.

See also: residual analysis, regression diagnostics, model diagnostics, outlier detection.