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OpenVerbs Linear Regression
x402
OpenVerbs Linear Regression is a paid API for AI agents from stats.openverbs.com, paid per call via x402, $0.004/call, status unknown (last checked 2026-09-15).
Fits a simple linear regression to paired x/y data arrays and optionally predicts y at a given x value
Price / call$0.004
Success—
StatusUnknown
How this service works
Ordinary-least-squares linear regression of y on x: slope, intercept, correlation r and coefficient of determination r². Pass predictAt to also evaluate the fitted line at a given x. Rejected as a clean 400 if lengths differ or x has zero variance.
Output
Returns the fitted linear regression parameters including slope, intercept, and R-squared (goodness-of-fit). If predictAt is provided, also returns the predicted y value at that x coordinate. The result enables the caller to understand the linear relationship between the two variables and make point predictions.
Example use cases
Sales trend forecasting
“I have monthly sales figures for the last 12 months. Can you fit a linear regression to this data and predict next month's sales? x is [1,2,3,4,5,6,7,8,9,10,11,12] and y is [4200,4500,4700,5100,5300,5600,5900,6100,6400,6700,6900,7200], predict at x=13.”
Physics experiment curve fitting
“I ran an experiment measuring distance over time and got these readings: time x=[0,1,2,3,4,5] seconds, distance y=[0.0,9.8,19.6,29.5,39.3,49.1] meters. Fit a linear model and tell me the slope and intercept.”
House price vs size estimation
“I have square footage and prices for 6 houses: sizes are [800,1000,1200,1500,1800,2100] and prices are [150000,185000,220000,275000,330000,390000]. Run a linear regression and estimate the price for a 1600 sq ft house.”
Technical details
Endpoint
POSThttps://stats.openverbs.com/v1/regressionWhen to prefer
Choose this endpoint when you need a quick, serverless linear regression computation without standing up your own statistical environment. It is ideal for simple bivariate relationships where a straight-line fit is appropriate, especially when you also want a point prediction at a specific x value. Prefer it over general-purpose compute endpoints when you want a clean, purpose-built statistical API that accepts raw numeric arrays directly.
Side effects
- x and y arrays of different lengths — validation error returned
- fewer than 2 data points provided — minimum constraint violated
- non-numeric values in x or y arrays — schema validation failure
- arrays exceeding 100,000 items — maxItems constraint rejection
- payment not provided or insufficient — 402 Payment Required
- perfectly vertical data (all x values identical) — undefined slope / division-by-zero error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Tags
x402
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