Quantile-quantile plot
A quantile-quantile plot compares sample quantiles with theoretical or reference-distribution quantiles to assess distributional agreement.
Statistics Slam Dunk
A normal QQ plot compares ordered residuals or observations with theoretical normal quantiles to assess compatibility with a normal distribution.
- How it is used
- Checks normality visually for continuous variables in exploratory analysis and for residuals in regression or ANOVA diagnostics.
- How deep it goes
- Explains diagonal-line and tail-deviation interpretation but defers formal distributional tests and quantile calculations.
- Examples
- Uses NBA regression residuals, ANOVA residuals, point spreads, and total-score prediction errors as plotted data.
- What you can do after
- Evaluate/understand whether continuous values or model residuals visually conform to a normal distribution.
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Statistics Every Programmer Needs
A Q–Q plot is a graph comparing sample quantiles with theoretical quantiles to assess whether data follow a specified distribution.
- How it is used
- Assesses whether linear-regression residuals satisfy the normality assumption.
- How deep it goes
- Explains visual alignment, tail deviations, subjectivity, and Jarque–Bera follow-up without developing the underlying quantile calculations.
- Examples
- Uses Python code and a residual Q–Q figure with points compared against a dashed normal-reference line.
- What you can do after
- Evaluate residual normality visually and pursue statistical confirmation with the Jarque–Bera test.
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Related concepts
- normal distributionuses · A normal distribution is a continuous probability distribution with a symmetric, bell-shaped density determined by its mean and variance.
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