{"uid":"cap_87yv8r8uN0XIXj5jzwRgS","slug":"hubvibe-statistical-analysis-095a2d7e","name":"HubVibe Statistical Analysis","description":"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. 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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?"},{"title":"Probability query on BigQuery dataset","prompt":"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?"}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-11T06:45:24.495Z","isFirstParty":false,"canonicalSlug":"hubvibe-statistical-analysis-095a2d7e"}