# Brand Mention Inventories — Two-Measure Correlation + Explicit Missing-Value Fill

> Brand Mention Inventories — Two-Measure Correlation + 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 2026-10-16).

Computes a two-measure correlation between mention_count and source_count, and fills missing values using a chosen strategy (zero, mean, or median), for up to 200 rows of brand mention inventory data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-18-184?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/brand-mention-inventories-two-measure-correlation-explicit-missing-6edbbe6b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_j_Bsjm-3PFiD4yPpQCCmk

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 brand-mention-inventories-two-measure-correlation-explicit-missing-6edbbe6b -d '<json body>'
```

Example prompt: I have up to 200 rows of brand mention inventory records with mention_count and source_count fields — some values are missing. Can you run a two-measure correlation between those fields and fill in the missing mention_count values using median imputation?

## When to prefer this

Choose this endpoint when you have brand mention inventory data (mention_count and source_count per row) and need both a correlation analysis between the two measures and a missing-value imputation in a single paid call. Ideal for offline or batch analytical workflows where no live data lookup, prediction, or external enrichment is needed. Best suited for datasets up to 200 rows requiring fast, local statistical computation.

## Known failure modes

- Exceeds 200-row limit — request rejected
- Missing required fields (row_id, date, company_group) — validation error
- Invalid date format (not YYYY-MM-DD) — schema error
- No numeric values available for mean/median computation when all values are null — fill strategy may fail
- Invalid fill method or field enum value — schema rejection

## How this service works

Brand mention inventories — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + Explicit missing-value fill for supplied brand mention inventories records (mention_count, source_count). 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 correlation result showing the statistical relationship between mention_count and source_count across the supplied rows, and (2) a missing-value fill report for the specified field using the chosen strategy (zero, mean, or median). Both reports are computed locally without external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/brand-mention-inventories-two-measure-correlation-explicit-missing-6edbbe6b/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)