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Unit Return Rate – IQR Outlier Flags
x402
Unit Return Rate – IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).
Flags statistical outliers in unit return rate data using the interquartile range (IQR) method across up to 200 supplied retail records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Unit return rate - IQR outlier flags: IQR outlier flags for Unit return rate. Supplied retail figures only. Local calculation and scenario analysis; no live inventory, account access, platform fee lookup or order execution. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded iqr outlier flags deliverable.
Output
Returns outlier flag indicators for each supplied row, identifying which records have unit return rates that fall outside the IQR-defined bounds (typically below Q1 − 1.5×IQR or above Q3 + 1.5×IQR), based purely on the locally supplied data with no live inventory or platform data.
Example use cases
Flagging rogue SKUs in returns data
“I've got return and sales figures for 50 SKUs from last quarter — can you run IQR outlier detection on the return rates and tell me which SKUs are statistical outliers I should investigate?”
Quality control check after a product launch
“We just launched a new product line and I want to see if any items have return rates that are outliers compared to the rest of the range — here are the sold and returned unit counts for each item, can you flag the unusual ones using IQR?”
Retail audit for abnormal return patterns
“Before I present to the board, I need to check our store-level return rates — can you apply IQR outlier flagging to these 30 stores' sold and returned unit numbers and highlight any that fall outside the normal range?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-retail-unit-return-outliers?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a batch of retail records with sold and returned unit counts and want to statistically flag which records are outliers by return rate using the IQR method. It is ideal for local, scenario-based analysis without needing live inventory or platform integration. Prefer it over simple mean/threshold approaches when you want a robust, percentile-based outlier detection that is resistant to skewed distributions.
Side effects
- Fewer than 2 rows supplied — minimum is 2 rows
- sold_units value of 0 or missing (exclusiveMinimum enforced)
- returned_units exceeds sold_units in a way that produces implausible rates — accepted but may skew IQR
- More than 200 rows submitted — maxItems limit exceeded
- Malformed row objects with extra properties rejected due to additionalProperties:false
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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