A residual-versus-fitted plot displays residuals vertically against predicted responses horizontally. Curvature suggests an incorrect conditional-mean model; a fan-shaped spread suggests heteroscedasticity, while isolated large residuals may indicate unusual observations. It does not alone measure leverage or influence: regression leverage records unusual predictor combinations, and Cook's distance combines leverage and residual size.
The figure in coefficient of determination shows why the plot is useful: subtracting the fitted linear trend makes a small curved component easy to see. A visible pattern supplies a reason to investigate the model, while the absence of a pattern in a small sample does not establish that all assumptions hold. Other views address other questions: regression residuals against observation order can reveal temporal dependence, and a normal probability plot examines distributional shape. NIST's statistical handbook develops this use of graphical residual analysis for model validation. [ref]
https://www.itl.nist.gov/div898/handbook/pmd/section4/pmd44.htm. NIST, How can I tell if a model fits my data?
A scale-location plot compares the square root of the absolute standardized regression residual with the fitted values. Systematic changes in level or spread suggest heteroscedasticity. It assesses the error scale, while a residual-versus-fitted plot primarily reveals mean patterns as well as changing spread.
Articles by others on the same topic
There are currently no matching articles.