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Halowerk IoT Sensor Calibration — OLS Linear Fit
x402
Halowerk IoT Sensor Calibration — OLS Linear Fit is a paid API for AI agents from iot.halowerk.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-14).
Fits a linear calibration model (reference = slope × observed + offset) using ordinary least squares over paired sensor readings, returning slope, offset, and residual diagnostics.
Price / call$0.003
Success—
StatusUnknown
How this service works
Fits reference = slope × observed + offset with ordinary least squares over paired caller-supplied readings. It reports residual diagnostics and does not claim traceability, uncertainty accreditation or suitability beyond the supplied calibration range.
Output
Returns the fitted slope and offset (intercept) from the OLS regression, along with residual diagnostics such as per-point residuals, R-squared (coefficient of determination), and possibly root mean square error — indicating goodness of fit over the supplied calibration range.
Example use cases
Temperature sensor drift correction
“My temperature sensor has been reading slightly off — I have 10 paired observed and reference readings I collected in the lab. Can you fit a linear calibration so I get the slope and offset to correct future readings, plus tell me how well the fit performs?”
Pressure sensor factory calibration
“I need to calibrate our pressure sensors before shipping. Here are the observed sensor outputs [101.2, 200.5, 301.1, 399.8, 500.3] and the reference gauge pressures [100.0, 200.0, 300.0, 400.0, 500.0] — fit an OLS line and give me the calibration coefficients and residuals.”
Evaluating sensor linearity
“I want to check how linear my light sensor is across its range. I have paired observed and reference readings and need you to run an OLS fit and report the residual diagnostics so I can see if the sensor deviates significantly from linearity.”
Technical details
Endpoint
POSThttps://iot.halowerk.com/v1/sensor-calibrationWhen to prefer
Choose this endpoint when you have paired observed and reference sensor measurements and need a simple, fast linear calibration (slope + offset) with residual diagnostics. It is ideal for IoT sensor correction workflows where a straight-line model is sufficient and you do not need certified traceability, uncertainty budgets, or non-linear fitting. Prefer alternatives if you need polynomial or multi-variable regression, or accredited calibration certificates.
Side effects
- Arrays of different lengths return a validation error
- Fewer than 2 data points in either array triggers a minimum-items error
- Perfectly collinear or constant observed values may cause a degenerate fit
- Values outside ±1e12 are rejected by schema validation
- Mismatched or non-numeric input types return a 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| observed | array | |
| reference | array |
Tags
x402
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