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Saylor Innovations Data Desk – Linear Regression
x402
Saylor Innovations Data Desk – Linear Regression is a paid API for AI agents from saylorinnovations.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-27).
Performs simple linear regression on two numeric lists, returning slope, intercept, R², p-value, standard errors, 95% confidence interval, fitted equation, and an optional prediction for a new x value
Price / call$0.01
Success—
StatusUnknown
How this service works
Linear regression on two lists - slope, intercept, r, r-squared, standard errors, p-value, 95% slope interval, equation and a prediction for a new x
Output
Returns a JSON object with: number of data points (n), slope, intercept, Pearson r, R-squared, slope standard error, slope t-statistic, slope p-value, 95% confidence interval for the slope as a two-element array, the fitted line as a human-readable equation string, a plain-language interpretation, and (if predict_x was supplied) a prediction object containing the input x, predicted y, and a boolean indicating whether the prediction extrapolates beyond the observed data range.
Example use cases
Sales trend and forecast
“I have monthly sales figures for the past year. X is [1,2,3,4,5,6,7,8,9,10,11,12] and Y is [5200,5450,5700,5800,6100,6350,6500,6700,7000,7200,7400,7600]. Can you run a linear regression and predict what sales will be in month 15?”
Checking significance of a relationship
“I want to know if there's a statistically significant relationship between hours studied and test scores. Here are hours: [1,2,3,4,5,6,7,8] and scores: [55,60,65,70,72,78,85,90]. Give me the slope, p-value, and R-squared so I can see if it's meaningful.”
Break-even extrapolation for cost model
“Can you fit a regression line to my production cost data? Units produced are [100,200,300,400,500] and total costs are [1500,2200,2900,3700,4400]. I also need a cost prediction for 650 units — flag it if that's outside my data range.”
Technical details
Endpoint
GEThttps://saylorinnovations.com/api/stats/regression?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a complete, single-call simple linear regression with statistical significance testing (p-value, confidence interval) and optional point prediction, especially within an agent workflow where you want a structured JSON response plus a plain-language interpretation rather than running a stats library locally.
Side effects
- Mismatched x and y list lengths returns a 4xx validation error
- Fewer than 3 data points triggers a minimum-count error
- Non-numeric values in the comma-separated strings cause a parsing/validation error
- Perfect collinearity or zero variance in x may produce infinite or null standard errors
- predict_x outside data range returns a valid prediction but with extrapolated=true flag
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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