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SignalDesk AI Linear Regression
x402
SignalDesk AI Linear Regression is a paid API for AI agents from x402-agent.kariguetoazul.workers.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-13).
Computes simple linear regression (slope, intercept, R²) over two paired numeric arrays
Price / call$0.001
Success—
StatusUnknown
How this service works
Evidence-backed autonomous AI jobs and paid data tools. Queue/D1/cron run outside the chat; free quotas and bounded execution apply.
Output
Returns a JSON object with four fields: slope (rate of change of Y per unit X), intercept (Y value when X is zero), r_squared (coefficient of determination, 0–1 indicating goodness of fit), and observations (count of data points used in the regression).
Example use cases
Sales trend over time
“I have monthly sales figures for the last 12 months and want to find the trend line — X is month numbers 1 through 12 and Y is [4200, 4350, 4100, 4600, 5000, 5200, 5400, 5300, 5700, 6000, 6100, 6400]. What's the slope and how good is the fit?”
Price vs demand analysis
“Can you fit a regression line to my price and demand data? X prices are [10, 15, 20, 25, 30] and Y demand units are [500, 420, 370, 310, 250]. I want slope, intercept, and R-squared to understand how price drives demand.”
Height vs weight correlation
“I've got a dataset of heights in inches [60, 63, 65, 68, 70, 72, 74] and weights in pounds [120, 135, 145, 160, 172, 185, 200] — run a linear regression so I can see the best-fit line and how strong the relationship is.”
Technical details
Endpoint
POSThttps://x402-agent.kariguetoazul.workers.dev/api/stats/linear-regression?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, cheap ($0.001 USDC) server-side linear regression computation without setting up a local statistics library. It handles up to 1000 paired observations and returns all key regression statistics in one call. Prefer this over client-side computation when the agent environment lacks numpy/scipy, or when you need a lightweight, pay-per-use statistical computation in an autonomous workflow.
Side effects
- Arrays x and y have different lengths — request fails with validation error
- Fewer than 2 data points provided — minItems constraint violated
- Values exceed ±10^15 boundary — rejected by schema validation
- Payment of $0.001 USDC not completed via x402 — 402 Payment Required response
- Perfectly vertical data (all x values identical) — division by zero may cause error or undefined slope
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| x | array | At least two observations. |
| y | array | At least two observations. |
Output
Schema · Response
{
"type": "json",
"example": {
"slope": 2,
"intercept": 0,
"r_squared": 1,
"observations": 3
}
}Tags
x402
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