 Fitting Models to Data: Residual Analysis, a PrimerJulia Martin, David Daffos Ruiz De Adana, Alberto Romero Gracia and Agustin G. Asuero A chapter in Uncertainty Quantification and Model Calibration from IntechOpen Abstract: The aim of this chapter is to show checking the underlying assumptions (the errors are independent, have a zero mean, a constant variance and follows a normal distribution) in a regression analysis, mainly fitting a straight-line model to experimental data, via the residual plots. Residuals play an essential role in regression diagnostics; no analysis is being complete without a thorough examination of residuals. The residuals should show a trend that tends to confirm the assumptions made in performing the regression analysis, or failing them should not show a tendency that denies them. Although there are numerical statistical means of verifying observed discrepancies, statisticians often prefer a visual examination of residual graphs as a more informative and certainly more convenient methodology. When dealing with small samples, the use of the graphic techniques can be very useful. Several examples taken from scientific journals and monographs are selected dealing with linearity, calibration, heteroscedastic data, errors in the model, transforming data, time-order analysis and non-linear calibration curves. Keywords: least squares method; residual analysis; weighting; transforming data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:118800 DOI: 10.5772/68049 Access Statistics for this chapter More chapters in Chapters from IntechOpen |
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