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Sampling Observations Analytics: Record Quality Audit + String Normalisation + Two-Measure Correlation
x402
Sampling Observations Analytics: Record Quality Audit + String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs three local analytical reports on sampling observation records — a data quality audit, string normalisation, and correlation between sample_value and sample_weight — for up to 200 rows in a single call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation for supplied sampling observations records (sample_value, sample_weight). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Returns three independent analytical reports for the supplied rows: (1) a record quality audit flagging issues such as nulls, out-of-range values, and malformed fields; (2) a string normalisation report showing standardised versions of text fields like dataset_group; and (3) a two-measure correlation report quantifying the statistical relationship between sample_value and sample_weight. All computation is local with no external lookups.
Example use cases
Pre-analysis data quality check
“I have 50 sampling observation rows from our lab study — can you run a quality audit on them to flag any bad records, normalise the group name strings, and also tell me how correlated the sample values are with their weights?”
Survey weight validation
“Before I finalise my survey analysis, can you check these 120 survey sample records for quality issues, clean up the dataset_group labels, and compute the Pearson correlation between sample_value and sample_weight so I can see if weighting is consistent?”
Batch anomaly detection in experimental data
“We collected 200 experimental observations across three groups — can you audit them for record quality problems, normalise any inconsistent string fields, and give me the two-measure correlation between sample_value and sample_weight?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-204?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously audit data quality, normalise string fields, and compute correlation for sampling observation data with up to 200 rows in a single low-cost call. It is ideal when you want three bundled analytical reports without setting up separate pipelines, and when all computation should happen locally with no external data access or predictions required.
Side effects
- Empty rows array or rows exceeding 200 items returns a validation error
- Malformed date strings (not matching YYYY-MM-DD) cause row-level rejection
- sample_value or sample_weight outside ±1,000,000,000,000 triggers range error
- Missing required fields (row_id, dataset_group, date) cause schema validation failure
- All-null sample_value or sample_weight columns may produce undefined correlation result
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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