# Aggregation Checks — Two-Measure Correlation + Explicit Missing-Value Fill

> Aggregation Checks — 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 2026-10-23).

Computes Pearson correlation between source_sum and aggregate_sum columns and fills missing values in a chosen field using zero, mean, or median imputation, across up to 200 aggregation check rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-17-184?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/aggregation-checks-two-measure-correlation-explicit-missing-value-fill-99a5df6f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_bwSC3s1krKvs9b8Armdj7

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 aggregation-checks-two-measure-correlation-explicit-missing-value-fill-99a5df6f -d '<json body>'
```

Example prompt: I have up to 200 rows of aggregation check records each with a row_id, dataset_group, date, source_sum, and aggregate_sum — can you run a two-measure correlation between source_sum and aggregate_sum, and also fill any missing values in source_sum using median imputation?

## When to prefer this

Choose this endpoint when you have tabular aggregation check data with source_sum and aggregate_sum columns and need both a statistical correlation measure between those two fields and a missing-value imputation pass in a single API call. It is best suited for data reconciliation workflows, financial auditing, and pipeline QA where you want offline, local analytical computation (no external lookups) on up to 200 rows at low cost. Prefer alternatives if you need more than 2 analytical reports, more than 200 rows, live data enrichment, or a different pair of columns.

## Known failure modes

- Fewer than 1 row submitted — request rejected
- More than 200 rows submitted — exceeds row limit
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Both source_sum and aggregate_sum are null for most rows — correlation undefined or NaN
- Field enum value not one of source_sum or aggregate_sum — validation error
- Method enum not one of zero, mean, median — validation error
- row_id missing or empty string — required field error
- Insufficient non-null pairs for correlation — may return NaN or undefined correlation

## How this service works

Aggregation checks — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + Explicit missing-value fill for supplied aggregation checks records (source_sum, aggregate_sum). 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 Pearson correlation result quantifying the linear relationship between source_sum and aggregate_sum across all supplied rows, and (2) a missing-value fill report showing which rows had null values in the specified field and what values were substituted using the chosen method (zero, mean, or median). No external data is fetched; all analysis is performed on the supplied rows only.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/aggregation-checks-two-measure-correlation-explicit-missing-value-fill-99a5df6f/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)