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Sampling Observations — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill
x402
Sampling Observations — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes a secondary-measure weighted mean and fills missing values (zero, mean, or median) for up to 200 sampling observation records, returning two analytical reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + Explicit missing-value fill for supplied sampling observations records (sample_value, 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
Returns two analytical reports: (1) the secondary-measure weighted mean computed across all supplied rows using sample_weight as the weight factor, and (2) the dataset with missing values explicitly filled using the chosen method (zero, mean, or median) for the specified field (sample_value or sample_weight). Up to 200 rows processed per call.
Example use cases
Survey data weighted aggregation
“I have 150 survey response rows, each with a sample_value score and a sample_weight, and some values are missing. Can you fill the missing sample_values using the mean method and compute the weighted mean across all rows?”
Lab sample quality fill and average
“I've got a batch of 80 lab sample records with sample_value and sample_weight columns — about 10 of the sample_values are null. Please fill them with zeros and give me the weighted mean for the sample_value field.”
Group-segmented weighted mean report
“Here are 200 sampling observations across two dataset groups (group-a and group-b) — can you fill any missing sample_weights using median imputation and compute the secondary-measure weighted mean so I can compare the groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-187?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a weighted mean computation and explicit missing-value imputation on sampling observation data in a single call. It is especially suitable for survey, scientific, or statistical datasets with up to 200 rows where sample_weight varies by record and nulls must be handled before aggregation. Prefer this over generic computation tools when you need two bundled analytical reports at low cost ($0.01 USDC) without needing live lookups, predictions, or external data sources.
Side effects
- Exceeds 200 row limit — request will be rejected or truncated
- Missing required fields (row_id, date, sample_value, sample_weight, dataset_group) cause validation errors
- Date values not matching YYYY-MM-DD pattern will fail schema validation
- All weights are zero or null, making weighted mean undefined
- Enum values for method or field outside allowed set cause rejection
- Extremely large or small values near ±1,000,000,000,000 boundary may cause computation edge cases
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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