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Data Reconciliation — IQR Outlier Flags + Secondary-Measure Weighted Mean
x402
Data Reconciliation — IQR Outlier Flags + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Analyzes up to 200 data reconciliation records (source_total vs target_total) using IQR-based outlier detection and secondary-measure weighted mean calculations, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — IQR outlier flags + Secondary-measure weighted mean: IQR outlier flags + Secondary-measure weighted mean for supplied data reconciliation records (source_total, target_total). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports for the submitted reconciliation rows: (1) IQR-based outlier flags identifying which rows have source_total or target_total values outside the interquartile range, and (2) a secondary-measure weighted mean calculation across the dataset. Both reports are computed locally using shared analytical engines with no external lookups.
Example use cases
Outlier detection in monthly financial reconciliation
“I have 120 rows of monthly financial reconciliation data with source and target totals by dataset group and date — can you flag any IQR outliers and compute the weighted mean on the source_total field for me?”
ETL pipeline data quality check
“After our ETL run, I've got 80 reconciliation records comparing source_total and target_total across two groups. Can you run IQR outlier flagging and a weighted mean analysis on the target_total field to see what looks off?”
Cross-system data validation for audit
“We're prepping for an audit and need to verify our source vs target reconciliation records — I have 45 rows with dataset_group, date, source_total, and target_total. Can you run the IQR outlier and weighted mean reports on source_total?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-177?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 reconciliation records with source and target totals and need fast, local statistical analysis — specifically IQR-based outlier flagging and weighted mean computation — without requiring live lookups, predictions, or external data. Ideal for data quality checks, ETL validation, and audit preparation where deterministic statistical methods are sufficient and privacy or offline computation is preferred.
Side effects
- Submitting more than 200 rows returns an error — batch must be split
- Missing required fields (row_id, dataset_group, date, source_total, target_total) cause validation failure
- Dates not matching YYYY-MM-DD pattern are rejected
- Numeric values outside ±1,000,000,000,000 are rejected
- Fewer than 1 row submitted returns an error
- IQR computation may be unreliable with very small datasets (fewer than 4–5 rows)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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