# Data Collection Completeness — Numeric Distribution + Two-Measure Correlation

> Data Collection Completeness — Numeric Distribution + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).

Analyzes data collection completeness records by producing a numeric distribution report and a two-measure correlation report for up to 200 rows of observed vs. expected values.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-22-081?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-collection-completeness-numeric-distribution-two-measure-3b6b6029
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_-esrXD-_5pCh02iXxg9Vz

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 data-collection-completeness-numeric-distribution-two-measure-3b6b6029 -d '<json body>'
```

Example prompt: Can you run a numeric distribution and two-measure correlation analysis on my data collection completeness records? I have rows with course_group, date, observed_values, and expected_values — up to 50 rows — and I want to analyze the observed_values field.

## When to prefer this

Choose this endpoint when you have tabular data collection completeness records with observed and expected numeric values and need both a distribution summary and a correlation analysis in a single low-cost call. It is well-suited for education analytics, data quality audits, or pipeline monitoring where you want to understand both the spread of values and the relationship between two measures across up to 200 rows, without needing external data lookups or predictions.

## Known failure modes

- More than 200 rows submitted — exceeds maximum row limit
- Missing required fields (row_id, date, observed_values, expected_values) in one or more rows
- Date format not matching YYYY-MM-DD pattern
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)
- Empty rows array — minimum 1 row required
- Invalid field enum value (must be 'observed_values' or 'expected_values')
- Payment of 0.01 USDC not supplied or rejected

## How this service works

Data collection completeness — Numeric distribution + Two-measure correlation: Numeric distribution + Two-measure correlation for supplied data collection completeness records (observed_values, expected_values). 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 statistical reports: (1) a numeric distribution analysis of the selected field (e.g. observed_values) across the submitted rows, and (2) a two-measure correlation report quantifying the relationship between observed_values and expected_values — all computed locally with no external lookups or live data.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-collection-completeness-numeric-distribution-two-measure-3b6b6029/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)