normal_2d

Simulated points from a bivariate normal distribution

Five hundred paired coordinates sampled from a bivariate normal distribution. The data is centered near the origin with standard deviations indicating a relatively equal spread in both dimensions. The variables exhibit negligible correlation (0.026), suggesting independence. Normality tests for each variable yield high p-values, supporting the normal distribution assumption. These characteristics make it well-suited for demonstrating statistical visualization techniques in Vega and Vega-Lite, including scatter plots, density plots, heatmaps, and marginal histograms/density curves. It can also serve as a clean baseline for testing new visualization methods or for educational purposes in data visualization and statistics. A contrast to uniformly distributed data in uniform-2d.json

JSON 500 rows 2 fields 34 KB 8 gallery examples 8 examples

Fields

FieldTypeDistributionSummaryMissing
u

mean: 0.005, std: 0.192, range: [-0.578, 0.533], p-value: 0.680

 number -0.578 – 0.533 · mean 0.005 Missing 0
v

mean: -0.011, std: 0.199, range: [-0.534, 0.606], p-value: 0.763

 number -0.534 – 0.606 · mean -0.011 Missing 0

Profiled across all 500 rows.

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Pick two fields. Scroll to zoom, drag to pan. Both fields have values in 500 of 500 rows.Pick two fields. Both fields have values in 500 of 500 rows. Vega-Lite · input binding · scale bindingVega-Lite · input binding
Open This Chart in the Vega Editor

Preview

First 5 of 500 rows5 of 500 rows
uv
-0.17240994521642160.007374582889590287
0.196435152500106350.13236252089007686
-0.05003877259110454-0.08910432731027129
-0.115687640380811830.37888941407245746
0.25112463542965790.1009497029056371

Examples

Vega and Vega-Lite examples open in the Editor with this dataset loaded. Altair examples link to their Python source.

Provenance

Source
  • Generated Data
License
File
data/normal-2d.json · 34,398 bytes · View on GitHub