# Sampling Observations — Grouped Numeric Summary + IQR Outlier Flags

> Sampling Observations — Grouped Numeric Summary + 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 2026-10-15).

Computes grouped descriptive statistics and IQR-based outlier flags for up to 200 sampling observation records with sample_value and sample_weight fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-094?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/sampling-observations-grouped-numeric-summary-iqr-outlier-flags-a9743b27
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_YjhQ07zMPCLLprhYIPgsU

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability sampling-observations-grouped-numeric-summary-iqr-outlier-flags-a9743b27 -d '<json body>'
```

Example prompt: Can you run a grouped numeric summary with IQR outlier flags on these sampling observations? I have up to 50 rows each with a row_id, dataset_group, date, sample_value, and sample_weight — I want to analyze the sample_value field grouped by category.

## When to prefer this

Choose this endpoint when you have tabular sampling observation data (up to 200 rows) that needs grouped descriptive statistics and IQR-based outlier detection in a single call. It is ideal for quality assurance workflows, survey data review, scientific measurement analysis, or any scenario where you need to both summarize and flag extreme values across predefined categories, without requiring external data sources or prediction models.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) causes validation error
- Invalid date format (must match YYYY-MM-DD pattern) causes rejection
- All sample_value or sample_weight fields null for a group prevents meaningful summary
- Numeric values outside allowed range (-1e12 to 1e12) cause schema validation failure

## How this service works

Sampling observations — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + IQR outlier flags 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) a grouped numeric summary with descriptive statistics (mean, median, min, max, count, etc.) broken down by dataset_group, and (2) IQR-based outlier flags identifying which individual rows fall outside the interquartile range for their group — all computed locally without external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "sample_value",
     "sample_weight"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "sample_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "sample_weight": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "sample_value": 10,
     "dataset_group": "group-a",
     "sample_weight": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "sample_value": 20,
     "dataset_group": "group-b",
     "sample_weight": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "sample_value": 15,
     "dataset_group": "group-a",
     "sample_weight": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "sample_value": 30,
     "dataset_group": "group-b",
     "sample_weight": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "sample_value",
    "sample_weight"
   ],
   "type": "string",
   "default": "sample_value"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/sampling-observations-grouped-numeric-summary-iqr-outlier-flags-a9743b27/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)