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DataForge Linear Regression API
x402
DataForge Linear Regression API is a paid API for AI agents from dataforge-production-ba33.up.railway.app, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-14).
Fits a simple linear regression to paired x/y data points and returns slope, intercept, and R² correlation statistics
Price / call$0.005
Success—
StatusUnknown
How this service works
Low-cost statistical and analytical micro-APIs for agents and operations workflows.
Output
Returns a JSON object with four fields: slope (rate of change of y per unit x), intercept (y-axis crossing point of best-fit line), r (Pearson correlation coefficient), and r2 (coefficient of determination indicating proportion of variance explained). Values are floating-point numbers.
Example use cases
Sales trend line for forecasting
“I have monthly sales figures for the past 12 months — x is month number 1 through 12 and y is [4200, 4400, 4800, 5100, 5300, 5600, 5900, 6100, 6400, 6700, 7000, 7300]. Can you fit a linear regression and tell me the slope, intercept, and R-squared so I can see how consistently we're growing?”
Experimental data correlation check
“We ran a lab experiment and got these paired measurements — x: [0.5, 1.0, 1.5, 2.0, 2.5, 3.0] and y: [1.1, 2.3, 3.0, 4.1, 5.2, 6.0]. Can you do a linear regression and tell me the slope, intercept, and R² to see how well the relationship holds?”
Ad spend vs. conversions analysis
“I want to understand how ad spend drives conversions. Here are my weekly numbers — ad spend x: [500, 750, 1000, 1250, 1500, 2000] and conversions y: [23, 31, 44, 58, 67, 89]. Run a linear regression and give me the slope, intercept, and R-squared.”
Technical details
Endpoint
POSThttps://dataforge-production-ba33.up.railway.app/api/linear-regression?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, cheap linear regression statistics (slope, intercept, R, R²) for pairs of numeric arrays without setting up a local data science environment. At $0.005 per call it is ideal for agent workflows, automation pipelines, or operational dashboards that need statistical summaries on demand. Prefer this over a general-purpose compute service when the input is already clean numeric arrays and you only need simple linear fit statistics.
Side effects
- Mismatched array lengths between x and y cause a 400 error
- Fewer than two data points provided — regression is undefined
- Non-numeric values in arrays cause parsing or validation errors
- Payment not included or insufficient — 402 Payment Required response
- Empty arrays or null inputs return an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| x | array | |
| y | array | |
| points | array |
Output
Schema · Response
{
"type": "json",
"example": {
"r": 0.999,
"r2": 0.998,
"slope": 2.03,
"intercept": -0.03
}
}Tags
x402
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