# Account Research Coverage – Two-Measure Correlation + Explicit Missing-Value Fill

> Account Research Coverage – 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 2026-10-19).

Computes Pearson correlation between researched_accounts and target_accounts, and fills missing values using zero, mean, or median imputation, across up to 200 account research coverage records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-08-184?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/account-research-coverage-two-measure-correlation-explicit-missing-9d848fcd
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_pHtas2ZZPlWP4RLZaj-8O

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 account-research-coverage-two-measure-correlation-explicit-missing-9d848fcd -d '<json body>'
```

Example prompt: I have account research coverage data for two company groups across 4 dates — can you run a two-measure correlation between researched_accounts and target_accounts, and fill any missing values using median imputation? Here are the rows: [{row_id: 'r1', company_group: 'group-a', date: '2026-10-01', researched_accounts: 10, target_accounts: 1}, {row_id: 'r2', company_group: 'group-b', date: '2026-10-02', researched_accounts: null, target_accounts: 2}].

## When to prefer this

Choose this endpoint when you have raw account research coverage records (researched_accounts, target_accounts) that need both statistical correlation analysis and missing-value imputation in a single call. It is ideal for B2B sales analysts and RevOps teams who need to clean and correlate account coverage data without standing up their own analytics pipeline. Prefer this over general-purpose statistics APIs when you need both reports bundled at low cost ($0.01 USDC for up to 200 rows) and the data is already structured to match the schema.

## Known failure modes

- Fewer than 1 row submitted — request rejected
- More than 200 rows submitted — exceeds maximum batch size
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range (±1 trillion) — rejected
- All values for one measure are null — correlation cannot be computed
- Invalid method enum value (not zero/mean/median) — schema error
- Invalid field enum value (not researched_accounts/target_accounts) — schema error

## How this service works

Account research coverage — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + Explicit missing-value fill for supplied account research coverage records (researched_accounts, target_accounts). 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 Pearson correlation report quantifying the linear relationship between researched_accounts and target_accounts across the submitted rows, and (2) an explicit missing-value fill report showing which rows had nulls and what imputed values (zero, mean, or median) were substituted — all computed locally on the supplied data, no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/account-research-coverage-two-measure-correlation-explicit-missing-9d848fcd/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)