Data Visualization Explorer

Data Visualization

Data points 24

Correlation strength 0.70

Noise 1.0

Mean value

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Std deviation

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Correlation r

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Range

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How it works

Each of the n points carries two raw numbers: an independent value A spread evenly across the sample, and a dependent value B = corr·A + noise·ε, where ε is random jitter and corr is the correlation strength you set. The bar chart orders the points by A and draws B as height — good for comparing magnitudes across categories. The scatter plot places each point at (A, B) in space — good for spotting whether two variables move together. Bar color marks each value's tier relative to the mean: blue below, teal near, gold above.

mean  = Σxᵢ / n
std   = √( Σ(xᵢ − mean)² / n )
r     = Σ(Aᵢ−Ā)(Bᵢ−B̄) / √( Σ(Aᵢ−Ā)² · Σ(Bᵢ−B̄)² )

Data points — sample size n; more points make trends and correlation clearer, fewer points make noise dominate.
Correlation strength — how strongly B follows A; near ±1 the scatter collapses toward a line, near 0 the cloud looks random.
Noise — random jitter added to B; higher noise widens the spread and pulls the measured r away from the set correlation.
Correlation r — the Pearson coefficient computed live from the current sample, exactly as a real analytics dashboard would report it.

Real-world relevance: this is the same decision every analyst makes before opening a spreadsheet — bar charts for comparing categories, scatter plots for testing whether two measurements are related — and the same Pearson formula used by BI tools and A/B-test dashboards.

Below mean Near mean Above mean

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