Q-Q Plot Generator

Overview

The Q-Q Plot (Quantile-Quantile Plot) Generator helps you assess whether your data follows a specific theoretical distribution. Q-Q plots compare the quantiles of your sample data against the quantiles of a theoretical distribution - if your data matches the distribution, points should fall along a straight line. This interactive tool generates Q-Q plots for various distributions, performs normality tests, and helps you identify patterns like heavy tails, light tails, skewness, and outliers.

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Tips

• Q-Q plots compare sample quantiles to theoretical distribution quantiles
• Points falling on the reference line (y=x) indicate good fit to the distribution
• S-shaped pattern: skewed data (left curve = right skew, right curve = left skew)
• Points above line on right: heavy right tail (more extreme values than expected)
• Points below line on left: heavy left tail
• Points below line on right: light tails (fewer extreme values)
• Outliers appear as points far from the line at the extremes
• Shapiro-Wilk test: p < 0.05 suggests departure from normality
• Histogram provides context for Q-Q plot interpretation
• Sample size affects Q-Q plot appearance - small samples show more variability
• Normal Q-Q plots are most common, but test against other distributions too!