# HubVibe Statistical Analysis

> HubVibe Statistical Analysis is a paid API for AI agents from hubvibe-io.com, paid per call via x402, $0.5/call, status unknown (last checked 2026-10-11).

Runs deterministic OLS linear regression, normal distribution fitting, Student t and Jarque-Bera p-values, and prediction intervals over (x, y) point data or BigQuery tables

## Facts

- Endpoint: POST https://hubvibe-io.com/work/stats/probability?utm_source=zero.xyz
- Price: $0.5/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-11
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/hubvibe-statistical-analysis-095a2d7e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_87yv8r8uN0XIXj5jzwRgS

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability hubvibe-statistical-analysis-095a2d7e -d '<json body>'
```

Example prompt: Run a linear regression and normal distribution fit on these data points — [[1,2.1],[2,3.9],[3,6.2],[4,8.0],[5,10.1]] — with alpha 0.05, and also give me prediction intervals for x values 6 and 7.

## When to prefer this

Choose this endpoint when you need deterministic, reproducible statistical computations on (x,y) numeric data — especially OLS regression with proper standard errors and confidence intervals, normality testing via Jarque-Bera, or probability queries on a fitted normal distribution. Prefer it over general-purpose code execution when you want guaranteed determinism (fingerprint-based row sampling, not random), structured JSON output, and the ability to source data directly from BigQuery. It is not a substitute for full exploratory data analysis platforms but excels at targeted parametric statistics.

## Known failure modes

- Fewer than 2 data points provided — returns validation error
- BigQuery table not found or columns not numeric — returns table/column error
- alpha not strictly between 0 and 1 — returns schema validation error
- predict_x requested without linear_regression in metrics — may return missing dependency error
- probability_queries with conflicting or empty conditions — returns malformed query error
- max_rows below 3 — returns validation error
- More than 100 predict_x values or 50 probability queries — returns limit exceeded error

## How this service works

Predictive probability and regression engine: least-squares linear regression with standard errors, confidence and prediction intervals, a fitted normal distribution with quantiles and probability queries (P(y > t)), exact Student t and Jarque-Bera p-values. Points inline or from a BigQuery table. Deterministic arithmetic, no LLM: the same input always returns the same numbers. Input: points or table with x_column, y_column; optional metrics, predict_x, probability_queries.

## Output

Returns a structured JSON object with results for each requested metric: for linear_regression — slope, intercept, R², standard errors, and confidence intervals; for normal_distribution — mean, std dev, fitted quantiles, and probability query results; for p_values — Student t and Jarque-Bera test statistics and p-values validated against the supplied alpha; for prediction — predicted y values and prediction intervals for each requested x.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "alpha": {
   "type": "number",
   "exclusiveMaximum": 1,
   "exclusiveMinimum": 0
  },
  "table": {
   "type": "string"
  },
  "points": {
   "type": "array",
   "items": {
    "oneOf": [
     {
      "type": "array",
      "items": {
       "type": "number"
      },
      "maxItems": 2,
      "minItems": 2
     },
     {
      "type": "object",
      "required": [
       "x",
       "y"
      ],
      "properties": {
       "x": {
        "type": "number"
       },
       "y": {
        "type": "number"
       }
      },
      "additionalProperties": false
     }
    ]
   },
   "maxItems": 100000,
   "minItems": 2
  },
  "metrics": {
   "type": "array",
   "items": {
    "enum": [
     "linear_regression",
     "normal_distribution",
     "p_values",
     "prediction"
    ],
    "type": "string"
   },
   "minItems": 1,
   "uniqueItems": true
  },
  "language": {
   "type": "string",
   "pattern": "^[A-Za-z]{2,3}(-[A-Za-z0-9]{2,8})*$",
   "maxLength": 35
  },
  "max_rows": {
   "type": "integer",
   "maximum": 100000,
   "minimum": 3
  },
  "x_column": {
   "type": "string"
  },
  "y_column": {
   "type": "string"
  },
  "predict_x": {
   "type": "array",
   "items": {
    "type": "number"
   },
   "maxItems": 100,
   "minItems": 1
  },
  "distribution_of": {
   "enum": [
    "y",
    "x",
    "residuals"
   ],
   "type": "string"
  },
  "probability_queries": {
   "type": "array",
   "items": {
    "type": "object",
    "properties": {
     "above": {
      "type": "number"
     },
     "below": {
      "type": "number"
     },
     "between": {
      "type": "array",
      "items": {
       "type": "number"
      },
      "maxItems": 2,
      "minItems": 2
     }
    },
    "maxProperties": 1,
    "minProperties": 1,
    "additionalProperties": false
   },
   "maxItems": 50
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json"
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/hubvibe-statistical-analysis-095a2d7e/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from hubvibe-io.com](https://www.zero.xyz/host/hubvibe-io.com/llms.txt)