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Weighted Calibration Fit (Linear Regression)
x402
Weighted Calibration Fit (Linear Regression) is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.0265/call, status unknown (last checked 2026-10-20).
Fits a weighted linear model y = intercept + slope*x to 2–64 (x, y, weight) pairs using exact rational normal equations, returning fitted coefficients, residuals, and weighted SSE/RMSE.
Price / call$0.0265
Success—
StatusUnknown
How this service works
Fit y = intercept + slope*x to 2–64 declared pairs using positive weights and exact rational normal equations. Returns residuals and weighted SSE rounded to 12 decimal places, plus finite float64 RMSE. Rejects degenerate X values; weights are not verified uncertainties. Not field certification.
Output
Returns the fitted intercept and slope from weighted normal equations, an array of per-point residuals (observed minus fitted), the weighted sum of squared errors (SSE) rounded to 12 decimal places, and a finite float64 RMSE. Also includes offer metadata (offerId, version, inputDigest, receiptDigest, amountBaseUnits) and a boundaries array.
Example use cases
Instrument calibration curve fitting
“I have 8 calibration readings from my sensor with known precision weights — x=[0,10,20,30,40,50,60,70], y=[0.2,10.1,19.8,30.3,40.1,49.7,60.4,70.2], weights=[2,2,3,3,2,3,2,2] — fit a weighted linear model and give me the slope, intercept, and residuals so I can characterize the sensor.”
Quality control regression with weighted observations
“I have 6 synthetic quality-control data points where some measurements are more reliable than others: x=[5,10,15,20,25,30], y=[11.2,21.5,31.1,41.8,51.3,60.9], weights=[1,3,3,2,3,1]. Can you run a weighted least squares fit and report the RMSE and weighted SSE?”
Analytical chemistry standard curve
“I'm building a standard curve for an assay using 4 public concentration-response pairs: x=[0.1,1.0,10.0,100.0], y=[0.05,0.48,4.93,49.7], weights=[1.5,2.0,2.0,1.0]. Fit a weighted linear regression and tell me the slope, intercept, and residuals rounded to 12 decimal places.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/micro/weighted-calibration-fit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need exact rational arithmetic weighted linear regression (y = intercept + slope*x) on small datasets (2–64 points) with per-point positive weights, and require residuals and SSE rounded to 12 decimal places with a finite RMSE. Prefer it over general-purpose statistics libraries when you need a deterministic, auditable microservice result with a receipt digest, or when operating in an agent pipeline that requires pay-per-call accountability via x402.
Side effects
- Fewer than 2 or more than 64 pairs provided — request rejected
- Degenerate X values (all x identical, causing singular normal equations) — rejected with error
- Non-positive weights cause rejection (weights must be positive)
- Invalid dataClass enum value (must be 'public' or 'synthetic')
- Payment not provided or insufficient USDC — x402 payment required
- Malformed JSON body or missing required fields
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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