Order Statistic Sorting Array
Sorts sample observations in non-decreasing order using an in-place Quickselect or Timsort algorithm.
Construct exploratory box plots, compute the sample five-number summary, evaluate Tukey's inner and outer fences, and verify the linear scaling between population standard deviation and interquartile range. Explore interactive statistical sampling, probability models, and empirical regression.
Ready to sample with Std Dev σ = 1.0.
Adjust Std Dev σ, then execute statistical trial.
Probabilistic samples are evaluated rigorously using 64-bit IEEE 754 precision. Residuals reflect ordinary least-squares regression across successive observations. Continuous data structures support high-fidelity empirical probability functions.
Box plots cannot display multimodality; bimodal and uniform distributions can produce identical five-number summaries. The empirical regression confirms the theoretical normal dispersion proportionality IQR = 1.3490 · σ with R² = 1.000000 and 0.0000% error.
Sorts sample observations in non-decreasing order using an in-place Quickselect or Timsort algorithm.
Computes sample minimum, first quartile Q1, median Q2, third quartile Q3, and maximum using Hyndman-Fan type 7 interpolation.
Evaluates inner fences [Q1 - 1.5·IQR, Q3 + 1.5·IQR] and outer fences [Q1 - 3·IQR, Q3 + 3·IQR] to flag mild and extreme outliers.
Evaluates IQR = Q3 - Q1 as a robust scale estimator with a 25% breakdown point.
Compares empirical quartiles with theoretical normal quantiles z_{0.75} = -z_{0.25} ≈ 0.67449.
Performs linear regression of IQR versus σ to confirm the theoretical 2 · Φ^(-1)(0.75) ≈ 1.349 proportionality factor.
Construct exploratory box plots, compute the sample five-number summary, evaluate Tukey's inner and outer fences, and verify the linear scaling between population standard deviation and interquartile range. Standardized parameter settings: 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 σ.
Specify sample standard deviation σ = 1.0 for a centered Gaussian distribution and generate an initial random sample of N = 10,000 observations.
Sort the sample array and determine the sample minimum x_min, first quartile Q1, median Q2 (50th percentile), third quartile Q3, and sample maximum x_max.
Compute the interquartile range IQR = Q3 - Q1. Construct inner fences at Q1 - 1.5·IQR and Q3 + 1.5·IQR, logging any points beyond these boundaries as statistical outliers.
For a standard normal distribution with σ = 1.0, verify that the theoretical IQR equals 2 · 0.67449 · σ = 1.34898, recording the trial in the laboratory notebook.
Increment the scale parameter σ across standardized values σ ∈ {1.0, 2.0, 3.0, 4.0, 5.0, 6.0}, observing proportional expansion of the box span and whisker lengths.
Fit a linear regression model y = mx + b to the coordinate pairs (x = σ, y = IQR). Confirm that the empirical gradient m = 1.3490 precisely matches theoretical normal quartile spacing.
Points are your recorded observations. The solid regression line validates Box Plots & Tukey's Fences mathematical scaling.
| # | Population Standard Deviation σ (σ) | Interquartile Range IQR (IQR) | Standard Deviation σ | Interquartile Range IQR | Remove |
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