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KiHustle Spearman Rank Correlation API
x402
KiHustle Spearman Rank Correlation API is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-13).
Computes the Spearman rank correlation coefficient between two numerical 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 Spearman correlation value (ranging from -1 to +1) and a 'status' field indicating whether the computation succeeded. A value near +1 indicates strong positive monotonic correlation, near -1 indicates strong negative monotonic correlation, and near 0 indicates no monotonic relationship.
Example use cases
Marketing spend vs sales rank correlation
“I have monthly marketing spend figures [1000, 1500, 2000, 1800, 2500] and corresponding sales rank scores [3, 5, 6, 5, 8] — can you compute the Spearman rank correlation to see if higher spend tends to produce better sales ranks?”
Survey response consistency check
“I ran the same survey twice and got two sets of ordinal ratings from the same respondents: [4, 3, 5, 2, 1] and [4, 2, 5, 3, 1]. Can you compute the Spearman correlation to tell me how consistent respondents were between the two rounds?”
Stock ranking correlation over time
“I have two lists of stock performance ranks from different periods — [1, 5, 3, 7, 2, 6, 4] and [2, 4, 3, 6, 1, 7, 5] — can you run a Spearman rank correlation to see if the top performers tend to stay on top?”
Technical details
Endpoint
POSThttps://kihustle.tech/bot-bazaar/api/v1/json-correlation-spearman?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a non-parametric rank-based correlation measure that does not assume a normal distribution or linear relationship — ideal for ordinal data, ranked scores, or data with outliers. Prefer over Pearson correlation when the data may not be normally distributed or when you care about monotonic rather than strictly linear association. At $0.002 per call, it is cost-effective for lightweight statistical computations in automated workflows.
Side effects
- Mismatched series lengths (series_a and series_b must have equal number of elements)
- Empty arrays provided as input
- Non-numeric values in either series causing parsing errors
- Network timeout or payment processing failure via x402 protocol
- Ambiguous or missing input schema fields returning a 400-level error
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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