QuantOracle Polynomial Regression

x402

QuantOracle Polynomial Regression is a paid API for AI agents from api.quantoracle.dev, paid per call via x402, $0.008/call, status unknown (last checked 2026-09-13).

Fits a polynomial regression model of a specified degree to X/Y data and returns coefficients, predictions, and model quality statistics (R², AIC, BIC, MSE).

Price / call$0.008

Seller volume →—

Success—

StatusUnknown

How this service works

QuantOracle: stats/polynomial-regression

Output

Returns polynomial coefficients, predicted Y values for each input X, R-squared, adjusted R-squared, MSE, AIC, BIC, the polynomial degree used, and computation time in milliseconds.

Example use cases

Example prompt

“Fit a degree-2 polynomial regression to my data — x values are [1, 2, 3, 4, 5] and y values are [2.1, 4.0, 9.2, 16.1, 25.3] — and give me the coefficients, predicted values, and R-squared.”

Technical details

Endpoint

POSThttps://api.quantoracle.dev/v1/stats/polynomial-regression

When to prefer

Use this endpoint when you need to model a nonlinear (curved) relationship between two numeric variables and want full model diagnostics (AIC, BIC, MSE, R²) alongside predictions. Prefer it over linear regression when your data has curvature, and over generic ML models when you need interpretable polynomial coefficients and statistical model-selection criteria.

Side effects

Mismatched x and y array lengths returns a 400 validation error
Degree greater than number of data points minus 1 causes underdetermined system error
Non-numeric values in x or y arrays return a parsing/validation error
Very large degree values may cause numerical instability or timeout
Empty arrays return a 400 bad request error

Input

{
  "input": {
    "body": {
      "x": [
        1,
        2,
        3,
        4,
        5
      ],
      "y": [
        2.1,
        4,
        9.2,
        16.1,
        25.3
      ],
      "degree": 2
    },
    "type": "http",
    "method": "POST",
    "bodyType": "json"
  }
}

Schema · Request body

Field	Type	Description
x	array	Independent variable array
y	array	Dependent variable array
degree	integer	Polynomial degree (1=linear, 2=quadratic, etc.)

Output

{
  "ms": 11.23,
  "aic": 3.26,
  "bic": 2.09,
  "mse": 0.033829,
  "_meta": {
    "url": "https://quantoracle.dev",
    "docs": "https://api.quantoracle.dev/docs",
    "powered_by": "QuantOracle"
  },
  "degree": 2,
  "predicted": [
    1.9686,
    4.3257,
    9.0114,
    16.0257,
    25.3686
  ],
  "r_squared": 0.9995,
  "coefficients": [
    1.94,
    -1.135714,
    1.164286
  ],
  "adjusted_r_squared": 0.9991
}

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Technical details

Endpoint

POSThttps://api.quantoracle.dev/v1/stats/polynomial-regression

When to prefer

Side effects

Mismatched x and y array lengths returns a 400 validation error
Degree greater than number of data points minus 1 causes underdetermined system error
Non-numeric values in x or y arrays return a parsing/validation error
Very large degree values may cause numerical instability or timeout
Empty arrays return a 400 bad request error

Input

{
  "input": {
    "body": {
      "x": [
        1,
        2,
        3,
        4,
        5
      ],
      "y": [
        2.1,
        4,
        9.2,
        16.1,
        25.3
      ],
      "degree": 2
    },
    "type": "http",
    "method": "POST",
    "bodyType": "json"
  }
}

Schema · Request body

Field	Type	Description
x	array	Independent variable array
y	array	Dependent variable array
degree	integer	Polynomial degree (1=linear, 2=quadratic, etc.)

Output

{
  "ms": 11.23,
  "aic": 3.26,
  "bic": 2.09,
  "mse": 0.033829,
  "_meta": {
    "url": "https://quantoracle.dev",
    "docs": "https://api.quantoracle.dev/docs",
    "powered_by": "QuantOracle"
  },
  "degree": 2,
  "predicted": [
    1.9686,
    4.3257,
    9.0114,
    16.0257,
    25.3686
  ],
  "r_squared": 0.9995,
  "coefficients": [
    1.94,
    -1.135714,
    1.164286
  ],
  "adjusted_r_squared": 0.9991
}