stats-regression

x402

stats-regression is a paid API for AI agents from x402.agentindex.world, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-12).

Performs ordinary least squares (OLS) simple linear regression on two numeric arrays, returning slope, intercept, and R-squared.

Price / call$0.001

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StatusUnknown

How this service works

Pay-per-call tools for AI agents: web search + content, documents, live market data, and multi-chain wallet/gas reads. Mainnet test from $0.000001 USDC on Base. Pay-per-call tools for AI agents in USDC on Base (x402/MPP): search, pdf, web-read, extract, summarize, fact-check, translate, jobs, discover. See GET /capabilities.

Output

Returns the OLS regression slope (rate of change of y per unit x), intercept (y-value when x is zero), and R-squared (coefficient of determination, 0–1, indicating how much variance in y is explained by x).

Example use cases

Sales trend line fitting

“I have monthly sales figures for the past year — x is [1,2,3,4,5,6,7,8,9,10,11,12] and y is [4200,4500,4800,5100,5300,5600,5900,6100,6400,6700,7000,7300]. Fit a linear regression to those and give me the slope, intercept, and R-squared so I can see how consistent the growth is.”

Student study hours vs grades

“I want to see how well study hours predict exam scores. Here's my data — hours: [1,2,3,4,5,6,7,8] and scores: [55,60,65,70,72,80,85,92]. Can you run OLS regression on those and tell me the slope, intercept, and R-squared?”

Temperature vs energy consumption correlation

“Run a linear regression for me on these temperature readings and electricity usage numbers — x temperatures are [30,35,40,45,50,55,60,65] and y usage values are [200,220,260,290,330,380,420,470]. I need the slope, intercept, and how good the fit is.”

Technical details

Endpoint

GEThttps://x402.agentindex.world/stats/regression?utm_source=zero.xyz

When to prefer

Use this endpoint when you need a fast, pay-per-call OLS simple linear regression (single predictor) with R-squared, without setting up a local stats library or managing API keys. Ideal for agents doing ad-hoc analytics, validating linear trends, or building lightweight data pipelines. For multiple regression or non-linear fitting, look elsewhere.

Side effects

Arrays of different lengths cause a validation error
Arrays with fewer than 2 elements are rejected (minItems: 2)
Non-numeric values in either array cause a schema validation failure
Perfectly vertical data (zero variance in x) causes division by zero / undefined slope
Payment failure or insufficient funds returns HTTP 402

Input

Schema · Request body

Field	Type	Description
x	array
y	array

Output

Schema · Response

{
  "type": "json",
  "example": {
    "slope": 2,
    "intercept": 0,
    "r_squared": 1
  }
}

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