Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Researchers · Anomaly Detection

Anomaly Detection for a correlation study

Built for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model. Simulate a correlation study live below — adjust the inputs and watch it respond, right in your browser.

Distributions

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Probability DistributionsLive

PDF / PMF explorer

Controls

Presets

Mean μ

Std dev σ

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Data Inspector

Distributionnormal

Mean0.000

Variance1.000

Std dev1.000

Governing equation

f (x) = \frac{1}{2 π 1.00} e^{- \frac{( x - 0 ) ^{2}}{2 \cdot 1.00}}

Reading this result: The mean μ only slides the bell sideways while σ sets its width — variance is σ² = 1.00, so doubling σ quadruples the spread.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Kmeans

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k-Means ClusteringLive

Lloyd's algorithm · live

Controls

Speed1×

Presets

k (centroids)

True clusters

Lloyd's algorithm: assign each point to its nearest centroid, then move each centroid to the mean of its members. Repeat until stable. Try setting k different from the true cluster count.

▶ Run in Python

Data Inspector

Iteration0

Inertia0.00e+0

Governing equation

J = k = 1 \sum 4 x \in C_{k} \sum ∥ x - μ_{k} ∥^{2}, μ_{k} = \frac{1}{∣ C _{k} ∣} x \in C_{k} \sum x

Reading this result: k matches the 4 true clusters, so each centroid can settle onto one real group and inertia falls to a clean minimum.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Frequently asked questions

Is this good for researchers?: Yes — this version of "Anomaly Detection for a correlation study" is framed for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.