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Market Survey Observations — Base-100 Series + IQR Outlier Flags
x402
Market Survey Observations — Base-100 Series + 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 2026-10-19).
Computes base-100 indexed time series and IQR-based outlier flags for up to 200 market survey observation rows across respondent_count or sample_weight fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Market survey observations — Base-100 series + IQR outlier flags: Base-100 series + IQR outlier flags for supplied market survey observations records (respondent_count, sample_weight). 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
Two analytical reports: (1) a base-100 indexed time series for the specified field (respondent_count or sample_weight), rebased relative to the row at base_index, and (2) IQR outlier flags indicating which rows fall outside the interquartile range — all computed locally from the supplied data with no external lookups.
Example use cases
Detect outlier panels in tracker study
“I have weekly tracker survey data for two brand groups over 20 weeks — can you rebase the respondent_count values to a base-100 index starting from week 1 and flag any weeks where the counts are IQR outliers? I'll give you the rows with row_id, company_group, date, respondent_count, and sample_weight.”
Normalize sample weights across segments
“Take these 50 rows of market survey observations with sample_weight values for three company groups and generate a base-100 indexed series anchored at row index 0, plus flag which rows are statistical outliers by IQR — I need two separate reports.”
Audit survey field data for anomalies
“I've got 80 survey observation records with dates, group labels, respondent counts, and sample weights — please index the sample_weight field to base-100 starting at position 0 and tell me which rows are IQR outliers across the dataset.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-06-164?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to normalize market survey time-series data to a common base-100 index AND simultaneously flag statistical outliers via IQR in a single low-cost call. It is ideal for survey datasets up to 200 rows where you want both indexing and outlier detection without building custom analytics. Prefer it over generic statistics APIs when working specifically with respondent_count or sample_weight fields grouped by company_group and date.
Side effects
- More than 200 rows submitted — endpoint rejects with validation error
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Missing required fields (row_id, company_group, date) causes rejection
- base_index value exceeds the number of submitted rows
- field value not in allowed enum (respondent_count, sample_weight) causes error
- Null values in the target field may affect base-100 calculation or outlier detection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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