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KiHustle Pearson Correlation Coefficient Calculator
x402
KiHustle Pearson Correlation Coefficient Calculator is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-10).
Computes the Pearson correlation coefficient between two numeric data series
Price / call$0.002
Success—
StatusUnknown
How this service works
Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.
Output
Returns a JSON object containing the Pearson correlation coefficient (r value) between the two input numeric series, along with a success status indicator. The r value ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no linear relationship.
Example use cases
Stock price correlation analysis
“I have daily closing prices for two stocks over the past month — can you calculate the Pearson correlation between Series A [102, 105, 103, 108, 110, 107] and Series B [55, 57, 54, 59, 61, 58] to see how closely they move together?”
Marketing spend vs revenue relationship
“Check the correlation between my monthly ad spend figures [500, 700, 650, 900, 1100] and the corresponding revenue numbers [4200, 5800, 5300, 7100, 8900] using Pearson — I want to know how strongly they're related.”
Sensor data synchronization check
“I have two sensor readings taken at the same intervals — series A is [0.5, 1.2, 1.8, 2.4, 3.1] and series B is [0.6, 1.1, 1.9, 2.5, 3.0] — can you run a Pearson correlation to see if they're measuring the same thing?”
Technical details
Endpoint
POSThttps://kihustle.tech/bot-bazaar/api/v1/json-correlation-pearson?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, inexpensive ($0.002 USDC) stateless Pearson correlation computation via a simple API call, especially in automated agent pipelines that already handle x402 micropayments. Prefer this over local computation when you need a hosted, consistent implementation or when working within an agent environment without access to statistical libraries.
Side effects
- Mismatched array lengths — series_a and series_b must have equal number of elements
- Empty arrays or single-element arrays may produce undefined or division-by-zero results
- Non-numeric values in either array will cause processing errors
- Zero-variance series (all identical values) will produce NaN or error due to undefined standard deviation
- Payment failure via x402 protocol returns 402 status before computation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| series_a | array | |
| series_b | array |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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