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agent402.tools Linear Regression
x402
agent402.tools Linear Regression is a paid API for AI agents from agent402.tools, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Fits a least-squares linear regression line to two data series, returning slope, intercept, R², and optional predictions for new x values.
Price / call$0.001
Success—
StatusUnknown
How this service works
Fit a least-squares line y = slope·x + intercept to two equal-length series. Returns slope, intercept, r² (variance explained), and optionally predicted y values for new x inputs - useful for trend extrapolation (e.g. project next quarter's revenue from the last 8 quarters).
Output
Returns the slope and intercept of the best-fit line, R² (coefficient of determination indicating variance explained), and optionally an array of predicted y values corresponding to any x inputs supplied in the predict field.
Example use cases
Example prompt
“Fit a linear regression to my quarterly revenue data — x is [1,2,3,4,5,6] and y is [120000,135000,148000,162000,179000,195000] — and predict what revenue will be at x=7 and x=8.”
Technical details
Endpoint
POSThttps://agent402.tools/api/linear-regressionWhen to prefer
Use this endpoint when you need a quick, statistically rigorous least-squares linear fit with R² and optional forward predictions, especially for trend extrapolation tasks like forecasting revenue, projecting metrics, or summarizing the linear relationship between two numeric series. Prefer it over manual calculation or general-purpose code execution when you want a clean API response without writing regression code yourself.
Side effects
- x and y arrays of unequal length — returns validation error
- empty or single-element arrays — insufficient data for regression
- non-numeric values in x or y arrays — parse/type error
- missing x or y fields — required field error
- predict values outside the range of x — extrapolation warning or silently extrapolated
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| x | array | Independent variable series (e.g. time) |
| y | array | Dependent variable series (same length as x) |
| predict | array | Optional x values to predict y for, using the fitted line |
Output
Schema · Response
{
"type": "json",
"example": {
"n": 5,
"slope": 2.01,
"equation": "y = 2.01x + 0.03",
"rSquared": 0.9997,
"intercept": 0.03,
"predictions": [
{
"x": 6,
"y": 12.09
},
{
"x": 7,
"y": 14.1
}
]
}
}Tags
x402
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