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agent402.tools Pearson Correlation
x402
agent402.tools Pearson Correlation is a paid API for AI agents from agent402.tools, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Computes the Pearson correlation coefficient (r), coefficient of determination (r²), and sample size (n) between two equal-length numeric arrays.
Price / call$0.001
Success—
StatusUnknown
How this service works
Pearson correlation coefficient between two equal-length numeric series. Returns r (the correlation, -1 to 1), r² (variance explained), n (sample size). Use this to ask things like: is a stock's daily return correlated with a macro indicator? Are two FRED series moving together?
Output
Returns a JSON object with three fields: r (Pearson correlation coefficient, ranging -1 to 1), r² (proportion of variance in y explained by x), and n (number of data points used in the calculation).
Example use cases
Example prompt
“Can you calculate the Pearson correlation between these two series — x: [1.2, 2.5, 3.1, 4.8, 5.0] and y: [2.4, 4.9, 6.0, 9.7, 10.1] — and tell me the r value, r², and sample size?”
Technical details
Endpoint
POSThttps://agent402.tools/api/correlationWhen to prefer
Use this endpoint when you need a fast, cheap ($0.001 USDC) computation of Pearson correlation between two numeric series without setting up a local statistics library. Ideal for AI agents that need on-the-fly correlation checks in workflows involving financial data, scientific measurements, or any paired numeric datasets.
Side effects
- Arrays x and y have different lengths — returns error
- Non-numeric values in either array — returns error
- Empty arrays provided — returns error or undefined result
- Single-element arrays (n=1) — correlation undefined
- All values in one series are constant (zero variance) — division by zero, returns error or NaN
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| x | array | First numeric series |
| y | array | Second numeric series (same length as x) |
Output
Schema · Response
{
"type": "json",
"example": {
"n": 5,
"r": 1,
"rSquared": 1,
"interpretation": "perfect positive linear relationship"
}
}Tags
x402
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