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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 2026-09-13).
Computes the Pearson correlation coefficient between two numeric data series
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object with a 'result' field containing the computed Pearson correlation coefficient (a value between -1 and 1 indicating the strength and direction of the linear relationship) and a 'status' field indicating success or failure of the computation.
Example use cases
Sales vs. advertising spend correlation
“I have monthly sales figures [12000, 15000, 13500, 17000, 20000] and the corresponding ad spend [1000, 1400, 1200, 1600, 2100] — calculate the Pearson correlation so I can see how tightly ad budget drives revenue.”
Temperature vs. energy consumption analysis
“Can you compute the Pearson correlation between daily high temperatures [72, 78, 85, 90, 88, 76] and daily electricity usage [310, 340, 390, 430, 415, 325]? I want to understand how strongly heat drives energy demand.”
Student test score relationship study
“I've got two sets of student scores — midterm grades [55, 67, 72, 80, 91, 88] and final exam grades [58, 65, 74, 83, 89, 92] — can you run a Pearson correlation to see if mid-term performance predicts final results?”
Technical details
Endpoint
POSThttps://kihustle.tech/agents/api/v1/json-correlation-pearsonWhen to prefer
Choose this endpoint when you need a quick, paid-per-call Pearson correlation computation without setting up your own statistical library or running local code. Ideal for AI agents that need on-demand correlation analysis as part of a larger workflow without a dedicated data science backend. At $0.002 per call it is cost-effective for occasional or moderate-volume correlation checks.
Side effects
- Mismatched array lengths between series_a and series_b causing computation error
- Empty arrays or single-element arrays that make correlation undefined
- Non-numeric values in either series causing parsing failure
- Zero-variance series (all identical values) causing division-by-zero in Pearson formula
- Missing series_a or series_b in request body
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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