Q-Q Plot Generator Q-Q Plot Generator Check normality (and more) in seconds. Paste your data, pick the distribution, and see the Q-Q plot with a reference line. 1. Paste your data Use comma, space or new line separated values. Example: 12, 10.5, 9, 13, 14.2 ... 12 10.5 9 13 14.2 11.7 10.9 12.5 9.5 15.1 2. Choose distribution & options Theoretical distribution Normal (estimate μ, σ) Uniform(0,1) Exponential(λ=1) Normal is the most common for normality tests. Reference line Least-squares fit line Line through quartiles None Generate Q-Q plot 3. Q-Q plot — 4. Sample vs Theoretical Quantiles # Ordered sample Theoretical quantile Difference How this Q-Q plot generator works This tool sorts your sample into order statistics \(x_{(1)}, \dots, x_{(n)}\) and pairs each one with a corresponding theoretical quantile from the chosen distribution. For the normal distribution, we first estimate the sample mean \(\hat{\mu}\) and standard deviation \(\hat{\sigma}\), then transform standard normal quantiles to match. Formula for the hyperbolic plotting positions We use the popular rule: p_i = (i - 0.5)

Calculators in Q-Q Plot Generator Q-Q Plot Generator Check normality (and more) in seconds. Paste your data, pick the distribution, and see the Q-Q plot with a reference line. 1. Paste your data Use comma, space or new line separated values. Example: 12, 10.5, 9, 13, 14.2 ... 12 10.5 9 13 14.2 11.7 10.9 12.5 9.5 15.1 2. Choose distribution & options Theoretical distribution Normal (estimate μ, σ) Uniform(0,1) Exponential(λ=1) Normal is the most common for normality tests. Reference line Least-squares fit line Line through quartiles None Generate Q-Q plot 3. Q-Q plot — 4. Sample vs Theoretical Quantiles # Ordered sample Theoretical quantile Difference How this Q-Q plot generator works This tool sorts your sample into order statistics \(x_{(1)}, \dots, x_{(n)}\) and pairs each one with a corresponding theoretical quantile from the chosen distribution. For the normal distribution, we first estimate the sample mean \(\hat{\mu}\) and standard deviation \(\hat{\sigma}\), then transform standard normal quantiles to match. Formula for the hyperbolic plotting positions We use the popular rule: p_i = (i - 0.5).