# Dataset Storage Analytics — Two-Measure Correlation + Missing-Value Fill

> Dataset Storage Analytics — Two-Measure Correlation + 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 used_mb and row_count, and fills missing values using a chosen strategy (zero, mean, or median), for up to 200 dataset storage records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-184?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/dataset-storage-analytics-two-measure-correlation-missing-value-fill-58888c7f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_32DsvwgTdwVhZ3X3nPhdz

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 dataset-storage-analytics-two-measure-correlation-missing-value-fill-58888c7f -d '<json body>'
```

Example prompt: Can you analyze my dataset storage records — I have up to 50 rows with used_mb and row_count fields — and compute the correlation between those two measures while also filling any missing used_mb values using the median strategy?

## When to prefer this

Choose this endpoint when you need to simultaneously assess the linear relationship between two storage metrics (used_mb and row_count) and clean missing values in a single API call, for small-to-medium datasets up to 200 rows. It is cost-effective at $0.01 USDC for both reports together. Prefer it over generic statistical libraries when you need a hosted, stateless, pay-per-call analytical service that requires no infrastructure setup.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Missing required fields (row_id, dataset_group, date) cause row-level errors
- Fewer than 2 non-null values may produce undefined or NaN correlation
- Invalid enum for method or field causes a 400-level error
- Payment not attached or insufficient USDC causes x402 payment-required response

## How this service works

Dataset storage — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + Explicit missing-value fill for supplied dataset storage records (used_mb, row_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 analytical reports: (1) a correlation result showing the statistical relationship (Pearson r) between used_mb and row_count across the submitted rows, and (2) the dataset with missing values in the specified field filled using the chosen strategy (zero, mean, or median). Both reports are computed locally from the supplied data with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-storage-analytics-two-measure-correlation-missing-value-fill-58888c7f/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)