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HubVibe Statistical Analysis
x402
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
Price / call$0.5
Success—
StatusUnknown
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.
Example use cases
Regression fit on sales data
“I have weekly sales figures paired with ad spend — points like [[1000,52000],[1500,73000],[2000,89000],[2500,105000],[3000,121000]] — can you run an OLS linear regression with alpha 0.05 and tell me the slope, intercept, standard errors, and confidence intervals?”
Normality test on experiment residuals
“After running my experiment I have these residuals: [-1.2, 0.4, 0.8, -0.3, 1.1, -0.7, 0.2, 0.9, -0.5, 0.6]. Can you fit a normal distribution to them and run a Jarque-Bera p-value test at alpha 0.05 to check if they're normally distributed?”
Probability query on BigQuery dataset
“I have a BigQuery table called `analytics.user_scores` with columns `session_length` and `conversion_score`. Can you fit a normal distribution to the y column and tell me the probability that conversion_score falls below 0.3 or above 0.9, using up to 50000 rows?”
Technical details
Endpoint
POSThttps://hubvibe-io.com/work/stats/probability?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| alpha | number | |
| table | string | |
| points | array | |
| metrics | array | |
| language | string | |
| max_rows | integer | |
| x_column | string | |
| y_column | string | |
| predict_x | array | |
| distribution_of | string | |
| probability_queries | array |
Output
Schema · Response
{
"type": "json"
}Tags
x402
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