# Sampling Observations — Numeric Distribution + IQR Outlier Flags

> Sampling Observations — Numeric Distribution + 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 numeric distribution statistics and IQR-based outlier flags for up to 200 sampling observation records (sample_value or sample_weight).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-080?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/sampling-observations-numeric-distribution-iqr-outlier-flags-a1f4d26d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_kktwAReCJg0HuK1P-c6_4

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-numeric-distribution-iqr-outlier-flags-a1f4d26d -d '<json body>'
```

Example prompt: Analyze the distribution of my sampling observations and flag any IQR outliers in the sample_value field — here are my 12 records: row-1 on 2025-01-10 with sample_value 42 and sample_weight 1, row-2 on 2025-01-11 with sample_value 200 and sample_weight 2, row-3 on 2025-01-12 with sample_value 38 and sample_weight 1, and so on for all 12 rows in group-a.

## When to prefer this

Choose this endpoint when you need both a numeric distribution summary and IQR outlier detection on a batch of up to 200 sampling observation records in a single call. It is ideal for scientific, environmental, or quality-control datasets with a sample_value and sample_weight structure. Prefer it over generic statistics APIs when you need two coordinated reports (distribution + outlier flags) computed together at low cost ($0.01 USDC) with no external data access or side effects.

## Known failure modes

- Fewer than 1 row submitted — validation error
- More than 200 rows submitted — exceeds row limit, request rejected
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- sample_value or sample_weight out of allowed range (±1,000,000,000,000) — validation error
- Invalid field selector (not 'sample_value' or 'sample_weight') — enum validation failure
- Missing required row fields (row_id, dataset_group, date) — schema validation error

## How this service works

Sampling observations — Numeric distribution + IQR outlier flags: Numeric distribution + 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 independent analytical reports: (1) a numeric distribution summary (e.g., mean, median, quartiles, min, max, std dev) and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range fence — all computed locally on the supplied data without external lookup.

## 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-numeric-distribution-iqr-outlier-flags-a1f4d26d/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)