Residual plot
A residual plot is a diagnostic graph of model residuals against fitted values, explanatory variables, or another relevant quantity to reveal systematic error patterns.
Statistics Every Programmer Needs
A residual plot is a graph displaying model residuals against fitted values or another explanatory quantity to reveal systematic error patterns.
- How it is used
- Uses residual-versus-fitted-value patterns to validate regression assumptions and evaluate whether time-series errors remain structured.
- How deep it goes
- Explains construction and visual interpretation, then connects patterns to linearity, variance, independence, and normality diagnostics.
- Examples
- Uses Matplotlib scatter plots, zero reference lines, residual-density plots, and ARIMA residual diagnostics as concrete examples.
- What you can do after
- Diagnose model assumptions by plotting residuals and identifying randomness, trends, curves, funnels, or remaining time-series correlation.
Read how Statistics Every Programmer Needs explains residual plot →
Statistics Slam Dunk
A residuals-versus-fitted plot displays model residuals against fitted values to reveal nonlinearity, nonconstant spread, or other systematic patterns.
- How it is used
- Checks regression and ANOVA model integrity by exposing residual patterns, linearity concerns, and unequal variances.
- How deep it goes
- Explains axes, residual meaning, pattern interpretation, and R plotting, but defers formal remedies for detected violations.
- Examples
- Uses NBA regular-season wins, points-category means, and R diagnostic plots for regression and ANOVA models.
- What you can do after
- Diagnose model assumptions with base R residuals-versus-fitted plots and interpret visible deviations.
Effective Data Analysis
A residual plot is a graph of model residuals against predicted values or explanatory-variable values used to assess error patterns.
- How it is used
- Diagnoses regression error patterns and supports decisions about data cleaning, transformations, and model iteration.
- How deep it goes
- Explains the diagnostic purpose and interpretation, then demonstrates plotting and follow-up investigation without covering residual theory in depth.
- Examples
- Uses daily rat-sighting predictions to identify outliers, missing weather data, and an adjusted residual plot.
- What you can do after
- Diagnose regression problems with a residual plot and investigate whether data removal or transformation improves the model.
Related concepts
- residual erroruses · Residual error is the difference between an observed value and its corresponding value predicted or fitted by a model.
- residual analysisused by · Residual analysis is the examination of differences between observed and model-predicted values to evaluate model fit, assumptions, and behavior.
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