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Linear Algebra Least-Squares Polynomial Fit
x402
Linear Algebra Least-Squares Polynomial Fit is a paid API for AI agents from linear-algebra.x402tookit.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-29).
Fits a least-squares line or polynomial (degree 1–6) to up to 100 data points, returning coefficients, R-squared, residuals, and optional curve evaluation.
Price / call$0.02
Success—
StatusUnknown
How this service works
Least-squares line or polynomial fit (degree 1 to 6, up to 100 points) with coefficients, R-squared and residuals.
Output
Returns a JSON object with the fitted polynomial coefficients (ordered from constant to highest degree), R-squared goodness-of-fit, slope and intercept (for degree-1), residuals array, RMSE, residual standard error, sum of squared residuals, fitted values at each input point, optional evaluated value at a requested x, number of points used, conditioning metadata (centering/scaling info), and any warnings or degraded-quality flags.
Example use cases
Trendline for sales growth
“I have monthly sales figures for the past 8 months: x=[1,2,3,4,5,6,7,8] and y=[120,145,160,188,210,240,275,310]. Can you fit a straight line (degree 1) and tell me the slope, intercept, and R-squared so I can see the growth rate?”
Quadratic fit for physics experiment
“I dropped a ball and recorded position vs time: x=[0,0.1,0.2,0.3,0.4,0.5] and y=[0,0.05,0.19,0.44,0.78,1.22]. Fit a degree-2 polynomial to these points and give me the coefficients and R-squared — I want to verify it follows a quadratic trajectory.”
Predict future value from historical data
“Here's historical temperature anomaly data: x=[1980,1990,2000,2010,2020] and y=[0.26,0.44,0.42,0.72,0.98]. Fit a degree-1 regression and evaluate it at x=2030 so I can see the projected value, and also give me R-squared.”
Technical details
Endpoint
GEThttps://linear-algebra.x402tookit.com/least-squares?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need least-squares regression with polynomial degrees 1 through 6 on moderate-sized datasets (up to 100 points), and want a single-call solution that returns not just coefficients but also R-squared, residuals, RMSE, and an optional point evaluation. Prefer it over general-purpose compute endpoints when the output format (coefficients, fit statistics, conditioning notes) matches your downstream needs directly.
Side effects
- Fewer data points than degree+1 causes underdetermined system error
- Mismatched x and y array lengths returns validation error
- Degree outside 1–6 range rejected
- More than 100 points exceeds limit
- Ill-conditioned data (e.g. duplicate x values) may return degraded flag or NaN coefficients
- Malformed JSON in x or y query params returns parse error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
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