[qq-basic] Basic Q-Q Plot (Quantile-Quantile)

[MarkusNeusinger]

Description

A Q-Q (Quantile-Quantile) plot compares the distribution of a dataset against a theoretical distribution (typically normal) or another dataset. Points falling along the diagonal reference line indicate the data follows the reference distribution.

Key Features

• Plots sample quantiles against theoretical quantiles
• Diagonal reference line for perfect distribution match
• Deviations from line reveal distribution differences
• Sensitive to skewness, heavy tails, and outliers

Applications

• Testing normality assumptions before statistical tests
• Identifying distribution characteristics (skewness, kurtosis)
• Comparing two empirical distributions
• Detecting outliers and data quality issues

Data Requirements

• sample (numeric) - Data to compare against reference
• Reference: Normal distribution (default) or another dataset
• Size: 20+ observations recommended for meaningful comparison

Visual Elements

• Scatter plot of ordered sample vs theoretical quantiles
• 45-degree reference line (y=x)
• Optional: confidence bands around reference line
• Clear axis labels indicating sample and theoretical quantiles

[claude]

✅ Spec ID: qq-basic

Rationale: Q-Q (Quantile-Quantile) plots are a fundamental diagnostic tool for distribution analysis. No similar specs exist in the repository. The ID qq-basic follows the naming convention for basic plot types.

Add label approved to begin generation.

[github-actions]

✅ Specification Ready: qq-basic

PR: #987

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specification.md

qq-basic: Basic Q-Q Plot

Description

A Q-Q (Quantile-Quantile) plot compares the distribution of a dataset against a theoretical distribution (typically normal) or another dataset. Points are plotted by matching sample quantiles to theoretical quantiles, with a diagonal reference line indicating perfect distribution match. Deviations from the line reveal distribution characteristics such as skewness, heavy tails, and outliers.

Applications

• Testing normality assumptions before parametric statistical tests (t-tests, ANOVA, regression)
• Identifying distribution characteristics such as skewness and kurtosis in research datasets
• Quality control analysis to verify process measurements follow expected distributions
• Detecting outliers and data quality issues in experimental data

Data

• sample (numeric) - The observed data values to compare against the reference distribution
• Reference: Normal distribution (default) with parameters estimated from sample
• Size: 30-500 observations recommended for meaningful visual comparison

Notes

• Points falling along the diagonal reference line indicate data follows the reference distribution
• Systematic deviations reveal specific distribution characteristics: S-curves indicate skewness, curved ends indicate heavy or light tails
• The 45-degree reference line (y=x) should be clearly visible
• Axis labels should indicate "Theoretical Quantiles" and "Sample Quantiles"

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[github-actions]

🚀 Specification Merged!

Specification: qq-basic
PR: #987 (merged)

The specification is now in main and synced to PostgreSQL.

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Available libraries: matplotlib, seaborn, plotly, bokeh, altair, plotnine, pygal, highcharts, letsplot

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